In this episode, Mary Krone explores her career shift from a PhD in chemistry and biochemistry to data science, where she builds financial credit models. She highlights her work’s tangible impact and discusses the challenges of work-life balance.Mary’s passion for data science’s positive potential in finance shines through as she debunks misconceptions, talks about career paths, and dives into the evolving world of data science and generative AI.The episode also includes topics of the need for continuous learning and the blend of art and science in data science. Highlights: Mary’s transition from doing theoretical work to work in the real world (00:34)It takes a “village” to be successful (10:06)Managing a team of data scientists and why she describes herself as “leading teams who use data science for good” (21:01)Mary’s views and optimism about the data science field (33:25)Women’s roles in the future of data science (45:07)Mentions:Connect with Mary Krone on LinkedInBios:Mary Krone believes in using data science for good––to make meaningful and positive impact. Currently, she leads a data science team at Credit Karma, a personal finance company. Previously, Mary held various leadership roles in both technical and management tracks at FICO. Mary holds a PhD in Chemistry & Biochemistry from UC Santa Barbara and a BA in Chemistry and Secondary Education from Vassar College.New co-host and the WiDS Chief of Programs, Chisoo Lyons spent years in consulting services, working with clients including leading banks and financial services organizations worldwide. She held several leadership positions in consulting, research, solution development, and business-line management. She kick-started her career as a data analyst at FICO. Today, at WiDS, she remains dedicated to supporting and empowering women in data science.Learn more from data science leaders like Mary on Data Science Leadership: Creating Meaningful Impact.Connect with UsChisoo Lyons on LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Kate Kolich serves as the Assistant Governor and the General Manager of Information Data and Analytics at the Reserve Bank of New Zealand. With an extensive background in the financial sector, she also has significant public sector experience. Throughout her impressive career, she's delved into areas like data analytics, digital strategy, information management, data governance, business intelligence, and data warehousing, among others. Soon after the launch of Women in Data Science (WiDS) at Stanford, Kate became an active WiDS ambassador. She has organized numerous WiDS conferences in New Zealand, spotlighting nearly 100 female data scientists. Beyond this, Kate is a passionate mentor and supporter of many professionals in New Zealand. In this episode, we discuss Kate's role at the Reserve Bank, the role of her team, highlights from her career, and her insights on being a successful woman leader in her field.For Detailed Show Notes visit our website.In This Episode We Discuss:Kate’s role at the Reserve Bank of New Zealand.Data Guardianship: the concept of ‘kaitiakitanga’(guardianship in Te re Māori) and its relevance for those working with data.Kate’s evolution from a hands-on tech role to impactful leadership.How Kate overcame self-doubt early on in her career.Championing innovative data visualizations at the EECA to create greater impact.The value Kate places on mentorship and helping others grow in their careers.Kate’s association with WiDS New Zealand: Organizing conferences and spotlighting female data scientists.Kate's journey of realizing the significance of leadership and communication for broader impact.RELATED LINKSConnect with Kate Kolich on LinkedInFind out more about the Reserve Bank of New ZealandView the EECA’s New Zealand Energy Scenarios Data Visualization View the data and statistics published by Kate’s team at RBNZ Statistics - Reserve Bank of New Zealand - Te Pūtea Matua (rbnz.govt.nz)Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide) Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Kate Kolich serves as the Assistant Governor and the General Manager of Information Data and Analytics at the Reserve Bank of New Zealand. With an extensive background in the financial sector, she also boasts significant public sector experience. Throughout her impressive career, she's delved into areas like data analytics, digital strategy, information management, data governance, business intelligence, and data warehousing, among others. Soon after the launch of Women in Data Science (WiDS) at Stanford, Kate became an active WiDS ambassador. She has organized numerous WiDS conferences in Algeria, spotlighting nearly 100 female data scientists. Beyond this, Kate is a passionate mentor and supporter of many professionals in New Zealand. In this episode, we discuss Kate's role at the Reserve Bank, the role of her team, highlights from her career, and her insights on being a successful woman leader in her field.For Detailed Show Notes visit: LINK TO WEBSITE LONG VERSION OF SHOW NOTESIn This Episode We Discuss:Kate’s role at the Reserve Bank of New Zealand.Data Guardianship: The philosophy of 'Maori' (guardianship in Te Reo Maori) and its relevance in protecting data.Kate’s evolution from a hands-on tech role to impactful leadership.How Kate overcame self-doubt early on in her career. Championing innovative data visualizations at the EECA to create greater impact.The value Kate places on mentorship and helping others grow in their careers.Kate’s association with WiDS New Zealand: Organizing conferences and spotlighting female data scientists.Kate's journey of realizing the significance of leadership and communication for broader impact.RELATED LINKSConnect with Kate Kolich on LinkedInFind out more about the Reserve Bank of New ZealandView the EECA’s New Zealand Energy Scenarios Data Visualization Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide) Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Telle Whitney began her career in the tech industry in 1986 after earning a Ph.D. in computer science from Cal Tech. Her journey into graduate studies was sparked by an encounter with graphics during her undergraduate studies at the University of Utah. Although she initially wasn't interested in graphics, the idea of computer-aided design fascinated her, and she was drawn to work with Ivan Sutherland, a co-founder of the computer science department at Cal Tech.Throughout college, Telle learned various programming languages, starting with C as an undergraduate and later delving into object-oriented languages like Simula and Mainsail. While she hasn't programmed in years, Telle acknowledges that programming languages evolve and change rapidly, but once you understand the core concepts, transitioning to a new language becomes relatively easy.Reflecting on her path into computer science, Telle admits that she had no exposure to the field during high school, which is a common experience for many young girls. “It wasn't until my sophomore year, where I was at my wit's end of trying to figure out what to study, and I took this interest test that compared your interests to other people's interests and programming came out on top.”From her first programming class, Telle knew she had found her calling, even though she started later than many of her peers. Telle's love for programming stems from its logical nature. “When you’re writing a program, and you’re trying to solve this problem, it is so absorbing. I would become completely captured with whatever I was working on at the time, and it was very fulfilling, no question.”She advises aspiring coders to ignore the myth of natural ability in programming and the notion that girls are not good at math. Persistence and patience are key in navigating the challenges that arise, and the belief in one's ability to succeed is crucial.Discussing the persistent stereotypes and biases that deter women and people of color from pursuing careers in tech, Telle, and Margot highlight the prevalence of these harmful beliefs even today. Despite efforts to increase diversity, Telle emphasizes that more needs to be done to ensure the best minds participate in shaping the future of technology. Both Telle and Margot stress the significance of representation, with Margot outlining the WiDS goal of achieving at least 30% female representation by 2030, given that the current representation stands at a mere 10%. Such representation can help drive a cultural shift and improve the treatment of underrepresented groups.Telle dedicated 20 years to working full-time in the chip industry, actively striving to bring about change within the field. Concurrently, she collaborated with her close friend Anita Borg on the Grace Hopper Celebration, an initiative aimed at celebrating women who create technology. When Anita fell ill with brain cancer, Telle was asked to step into the role of CEO. During her 15-year tenure, Telle successfully expanded Anita Borg into a prominent organization.Although she hadn't planned to take on this role initially, Telle saw it as a valuable opportunity and made a conscious pivot. She has since left Anita Borg to establish her own consulting firm, proud of the impact she made and the organization's continued influence under new leadership.The lack of progress in achieving diversity in the tech industry is a cause of concern for Telle. Breaking down barriers and changing the perception of what a technologist looks like remains an ongoing challenge.Telle's particular interest lies in fostering a more inclusive culture within organizations. While community plays a vital role, Telle believes that actual cultural change stems from providing equal opportunities for advancement.Offering advice to aspiring data scientists, Telle urges them to take risks, develop confidence in their ideas, and master effective communication. She emphasizes the importance of curiosity and creativity in shaping the future and encourages aspiring data scientists to be at the forefront of technological advancements. “I want you to be at the table creating a technology that’s going to change our lives. That’s what you should do.” RELATED LINKSConnect with Telle Whitney on LinkedInFind out more about AnitaB.orgConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)​Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Srujana Kaddevarmuth began her career near Bangalore, India after completing her master’s degree in engineering from Visvesvaraya Technological University. She has had a successful career in the tech industry and currently holds the position of senior director at Walmart's Data and Machine Learning Center of Excellence.In her role as senior director at Walmart, Srujana leads the AI portfolio for various aspects of the company's retail business, including omni retail, new and emerging businesses in the consumer and tech space, data monetization, and membership. Her primary responsibility is to drive innovation and promote the democratization of data and AI, aiming to create value for consumers, associates, and the business as a whole.Despite coming from an academic family, Srujana chose to pursue a career in the corporate sector rather than academia. After obtaining her bachelor’s degree in engineering, she gained real-world exposure to data science and AI while working at the Energy and Resources Institute. This experience fascinated her, leading her to pursue a master’s degree in engineering with an emphasis on operational research and data science.She then started her career as a data scientist at Hewlett-Packard, where she worked on market mix models in the consumer and marketing domain. Later, she led the big data analytics center of excellence at Hewlett-Packard and went on to work at Accenture, where she led a partnership with Google, developing various models for consumer hardware products before joining Walmart.Entering the corporate world after graduation, Srujana was surprised by the importance of collaboration in data science. She realized that building excellent algorithms alone is not enough; teamwork and collaboration are essential, particularly in applied data science.As a leader, Srujana prioritizes assigning projects to data scientists and AI experts based on their individual interests to keep them intellectually stimulated. She also empowers her team to make informed decisions based on available data. Her team is trained to use AI responsibly, with a focus on explainability, transparency, fairness, and bias elimination.With the increasing delegation of decision-making to algorithms, from trivial choices to significant ones in immigration systems, legal sentencing, and healthcare, it becomes crucial to protect consumer privacy and eliminate unintended consequences. Srujana explains that responsible generation and consumption of algorithms and data are paramount.One of Srujana's major challenges lies in creating proofs-of-concept that effectively translate into tech products and developing unbiased algorithms. “When we deploy these machine learning algorithms, many people fail to understand that these algorithms are the statistical representation of the world that we live in, and they may not necessarily be perfect and interpretable at times, as we have seen certain racist comments unleash on social media sites.” Addressing these issues, according to Srujana, requires eliminating signals of bias through careful data curation and training algorithms to avoid institutionalizing bias associated with certain data sets.Srujana is excited about the diverse advancements in data science, particularly in space exploration, healthcare, and agriculture. In addition to her work with Walmart, Srujana serves on the board of the United Nations Association, San Francisco chapter, where she utilizes data science to drive meaningful decision-making for the protection of our ecosystem.When asked what advice she would give her 18-year-old self, she responds that she would encourage herself to be open to the emerging field of data science and embrace its opportunities. Her advice for other data science enthusiasts is similar: “We have just started to open some new realms in the domain of data science and AI with generative algorithms as well as quantum computing, so I would just urge data science enthusiasts to be open to where this domain takes them.” RELATED LINKSConnect with Srujana Kaddevarmuth on LinkedInFind out more aboutWalmartLearn more about the United Nations Association San Francisco ChapterConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)​Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Today Veronica Edwards is a senior data analyst at Polygence, though her educational and career background encompasses a wide range – she has delved into everything from dance and choreography to physics, sociology, marketing, and most recently, data science. Polygence is a nonprofit that offers middle and high school students a 10-week research experience under the guidance of a professional mentor. As a senior data analyst at Polygence, Veronica uses data to help build and scale the company and to provide students and mentors with an optimal experience.Upon working at Polygence, Veronica was surprised to learn how little high school students are asked to do independent research. Independent research affords students the opportunity to explore their passions, get comfortable with the ambiguity of the research process, and become experts on their chosen topic. Polygence aims to democratize this research experience and has successfully targeted a diverse selection of program participants, attracting mentors and students in over 100 countries with a near-equal split of female and male participants. Growing up, Veronica trained vigorously as a ballet dancer alongside peers who aspired to be professional dancers, though she knew early on that she did not want to pursue a career in dance. Veronica believes her training as a dancer helped her build strength and perseverance that have served her throughout her career. Furthermore, the creativity she uses for dance and choreography informs her work as a data analyst, helping her to tell the story of the data she oversees.Veronica entered Princeton University as a physics major and then transitioned into sociology, where she saw how data could be used to understand society. While attending college, she explored different career paths through Princeton’s connections with the public sector. This led her to multiple internships in public service, including a marketing internship at Community Access, an NYC-based nonprofit. Upon graduation, she was accepted into a Princeton P-55 Fellowship, which connected her with her first job out of college as an executive assistant at ReadWorks, a nonprofit that helps K-12 students with reading comprehension. Veronica recalls a clear moment at ReadWorks that propelled her into data science. “The senior engineer was in the office one day and he asked me, ‘Veronica, do you want to learn how to pull data on your own?’ In that moment I didn’t know what SQL was, I had never heard [of] it before, but I said yes.”Veronica sees her non-technical background as an asset in data science because it allows her to think like other people, particularly those without technical backgrounds. “I come from a non-technical background, and so therefore for me, I'm a step ahead of people who do have a technical background, in explaining data because I know what it's like to not understand what's going on in a chart, for example, or what a P-value is.”When asked what advice she would give to herself 10 years ago, she says she would tell her not to write off subjects that she enjoys but isn’t the best at. “I was always decent at math and decent at statistics and pretty good at all of these subject matters, but I wasn’t the best. If I would have told myself back then [that] one day you’re going to have a career in data science, I would’ve been really intimidated, because that seems like something you need to have extremely high standards for.” Additionally, she would urge her younger self to be open-minded about her future plans, because in her words, “you never know what opportunities are going to present themselves.”RELATED LINKSConnect with Veronica Edwards on LinkedInFind out more about PolygenceConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)​Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

As Estée Lauder’s first-ever Chief Data Officer, Jane Lauder is combining data science with creativity to fuel the growth of the company. Jane has worked at Estée Lauder for 26 years – 24 of which she spent working on the brand and marketing aspects of the business. While working as the Global Brand President of Clinique, Jane saw the power of data to drive all aspects of the business, motivating her to transition into her current role. Estée Lauder Companies is one of the world’s leading manufacturers and marketers of luxury skincare, makeup, fragrance, and hair care products. It encompasses a house of about 30 brands including their flagship brand Estée Lauder, as well as other brands such as Clinique, Crème de la Mer, MAC, Jo Malone, Aveda, Le Labo, Bobbi Brown, Origins, Dr.Jart+, Too Faced cosmetics, and more. The beauty giant was founded by Jane’s grandmother, Estée Lauder, over 75 years ago. Before there were data and analytics to pull from, Estée Lauder would gather information about potential consumers by analyzing women’s bathrooms, paying close attention to details such as décor and the colors of their tiles. She then used this information to design aspirational product packaging that would elevate its surroundings. “In the beginning, we were a one-woman research company, and that one woman was Estée Lauder.”Today the company has a wealth of digital and in-store data. Jane and her team use this data to understand consumers’ aspirations better, gain insight into how different consumers use their products, and spot emerging trends in the cosmetic industry. This information helps them to respond to trends and tailor their products and messaging to meet consumers' unique needs and aspirations. As Estée Lauder’s Chief Data Officer, Jane’s biggest obstacle resides in deciding how to best utilize the ample data she has access to. Another obstacle lies in determining how to strike a balance between satisfying consumer needs today and investing in the future of the company. “You want to be able to use the data you have to create incredible opportunities, but also think about how to unlock the data for the future, and how to set up the foundational data sets, and data containers, if you will, to be able to create this quick analysis of the future.” Jane believes the future is promising for those seeking roles as data scientists within the cosmetics industry. The cosmetics industry is teeming with opportunities to connect with consumers and make a difference. 

An expert in climate change and the optimization of power grids, Priya Donti researches how to use machine learning for forecasting, optimization, and control of power grids to facilitate the integration of renewable energy. She first became interested in climate change during high school and studied computer science with a focus on environmental analysis as an undergraduate at Harvey Mudd College. After graduation, she spent a year on a Watson Fellowship, learning about different approaches for next-generation power grids in Germany, India, South Korea, Chile, and Japan. She went on to earn her PhD in power grid optimization at Carnegie Mellon. While there, she co-founded Climate Change AI, an initiative born out of a paper she co-wrote with academic and industry leaders about the ways machine learning could address climate change.Machine learning can play a role in mitigating climate change in areas like decarbonizing power grids, buildings, and transportation; helping create more precise forecasts for climate change impacts; and strengthening social, food, and health systems to cope with the impacts of climate change. There are several ways to apply machine learning to the climate crisis. One is distilling raw data into actionable insights, like turning satellite imagery into inputs on where the solar panels are or where deforestation might be happening, or turning large amounts of text documents into insights to guide policy or innovation. A second way is forecasting solar and wind power, and extreme weather events. A third is optimizing complex systems to make them more efficient, like heating and cooling systems in buildings or optimizing freight transportation systems. Machine learning is also valuable in science and engineering workflows to accelerate the design of new batteries or speed up climate or power models.While there are many ways that AI and data science can play a role in climate action, sometimes it’s difficult figuring out where to start. Priya says the WiDS Datathon is a great way to get started because no matter how much experience you have, you can enter and be able to work on this particular challenge. “The floor is low, but the ceiling in high.” There are also many resources on the Climate Change AI website to start learning, get involved, and meet other people working in the space through workshops, virtual happy hours, mentorship programs, and an online community platform. RELATED LINKSConnect with Priya on LinkedINFind out more about the Climate Change AIConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Lesly Zerna earned her undergraduate degree in Telecommunications Engineering at the Bolivian Catholic University and then traveled to Brussels to complete a Masters in Computer Science. After returning to Latin America, she began teaching data science and AI both in universities and virtual platforms and today her courses have thousands of online students. She brings insights from her experiences working in large companies overseas to her students in Latin America. For those just starting in data science, she says you must first identify your personal learning style (e.g., visual or text) to improve your learning experience and start with a general overview of the field. Next, find a practical topic you’re interested in, and look for projects, examples, authors, researchers who are working in that area. Do all of this while continuing to develop the fundamental skills you need (e.g., languages, platforms, frameworks) in data science. Lesly transmits her passion for learning to her students by using real scenarios instead of theory in textbooks. She lets them experience what works, shows the development process, and where common mistakes are made. She says it’s important for students to find where the problem is, know how to solve it, and make decisions. She believes there’s a lot to learn from the world of entrepreneurshipas you not only develop a project, you also have to develop the skills to explain and present the project, sell it, and negotiate. She believes that mentoring is essential to break down barriers for women. It can help dispel myths and biases about women in science and technology jobs, and learn from successful women that in spite of a hard path, they were able to achieve and follow their dreams.RELATED LINKSConnect with Lesly on LinkedINFind out more about the Universidad Privada BolivianaConnect with Cindy Orozco Bohorquez on LinkedIN

Leda Braga is the founder and CEO of Systematica Investments, a hedge fund that uses data science-driven models to support its investment strategies. Leda was born and raised in Brazil and found her way into the financial sector after getting her PhD in engineering and spending several years as an academic. Her financial career started with seven years in investment banking at JP Morgan and then she joined the hedge fund startup BlueCrest in 2000. She explains that while her funds did very well during the 2008 financial crisis, the time felt like an existential crisis because you didn’t know if the major investment banks were going to survive. But she said it was a formative time and she learned many lessons. Several years after the financial crisis, she spun off her own firm, Systematica Investments focused on systematic trading.Leda explains that systematic investment management is data science applied to investment. The systematic approach makes the investment process less reliant on the random nature of forecasting and more reliant on risk control in portfolio construction.Both discretionary traders and systematic traders are looking at information to try to make decisions. Those who do it on a discretionary basis tends to look at the data and make a decision to make money on a trade. Those that look at data on a systematic basis build data-driven processes for trading strategies for certain risk profiles and preferences that will produce consistent returns over time. She says the responsibility weighs heavily on her to ensure a good return because people's pensions are part of the money her firm manages.While she believes strongly in the power of leveraging data science in investment, we’re not yet at a point where AI allows us to do “autonomous investing” because there's a large element of randomness in markets and relatively sparse data so learning algorithms have limited use. She says that the only way it might be possible is if you’ve compartmentalized and narrowed the scope to the extent that you have a controlled amount of randomness. Learn more about Leda and systematic investing in her 2018 WIDS presentation, When Data Science is the Business.RELATED LINKSConnect with Leda on LinkedIn or TwitterFind out more about Systematica InvestmentsConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile​

A Colombian engineer, Jessica is fascinated by the processes and complexity of water supply systems in urban areas.In her post doc research in Australia, she brings together her expertise on the water hammer and transient flow waves to create an AI model that is able to identify where pipeline defects are faster and more accurately than existing techniques.She explains that in data science, the most important stage is understanding the problem. You need to bring in basic knowledge of the problem and expertise from other disciplines that are involved in a problem and combine that with artificial intelligence. AI is an important tool but just part of the solution. It’s critical to maintain all the legacy of knowledge and understanding of a problem. AI can make it simpler to apply, but you can’t leave behind the physics or knowledge of the hydraulic part of water movement. Working in industry, she has found that it’s important to first understand how the system works. In these large companies in charge of delivering water, each person has different objectives, so you need to understand how the company works, who is in charge, what are their objectives, and how they measure their success. If your research project aims at those things, they will be more receptive and a better chance of success.Jessica has learned in both research and industry consulting that nothing works the first time and it’s important to not to let those little defeats build up in your head. You need to trust yourself. There are many moments in life when you are criticizing yourself, and you realize that the biggest enemy you have is yourself. She just breaks down the problem into small parts and then solves each part one by one. She is passionate about teaching and inspiring young engineers about the importance of water and the future of this invaluable resource.RELATED LINKSConnect with Jessica on LinkedINFind out more about the University of AdelaideConnect with Cindy Orozco Bohorquez on LinkedIN

As a quantitative social psychologist, Karolina has always been interested in using data to measure human behavior to try to understand it better. She has researched questions around political attitudes and polarization, particularly in light of Brexit and Trump’s election in 2016. She wanted to understand how people could arrive at completely different understandings of the world and reflect it in their voting decisions. One of her findings was that in the American two-party political system, people tend to identify as either Republican or Democrat and are more likely to agree with statements from their identified party. People use identity cues as mental shortcuts to judge information because there’s simply too much information to decipher. She says the polarization is stronger in the US where there are just two major parties compared to other countries with more choice of multiple political parties.After her undergraduate and Ph.D. in psychology and two post-doctoral positions, Karolina decided to leave academia and to work for the nonprofit Teach First. She felt there was a lot of pressure in academia to become an expert in one niche and she wanted the freedom to pursue multiple topics that interest her. When she landed her first job outside of academia, she said the adjustment was a bit challenging, for example, when she first got the data to work with. In academia she knew exactly what the labels were, but in a new organization, she had to figure out how they measure things, what information they store, or what they use as a proxy for a certain behavior. As a researcher at Teach First, a non-profit in the UK that trains early career teachers to work with schools in disadvantaged areas, she is currently evaluating the impact of their programs in schools across the UK. She wants to know if their programs actually have an effect on the pupils that are being taught by their teachers compared to others.When reflecting on her career, she says there have been times when she questioned whether she had the right skills. She has learned that it’s OK to be uncomfortable in a new position. With any new challenge you take, it takes time to get to know that new environment, and get to a place where you can start confidently contributing. It’s part of growing and learning, the satisfaction that you get from crossing that bridge from being very unsure to getting to place where you're comfortable and succeeding is very rewarding. The process of maturing in your career is accepting that this is just going to be part of the journey.RELATED LINKSConnect with Karolina on LinkedIn or TwitterFind out more about Teach FirstConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

EPISODE NOTESWiDS Executive Director Margot Gerritsen welcomes her new co-host, Cindy Orozco, in a wide-ranging conversation about their career paths and valuable learnings along the way. Cindy is thrilled to be joining as podcast co-host and believes that showcasing women at all stages of their careers shows that we “share the same fears or experiences every day. It's just that some of us have been on the path a little bit longer than others.” Cindy is an applied mathematician who is currently working as a machine learning solutions engineer at Cerebras Systems. Originally from Colombia, she loved applied math, and did a master's in civil engineering and mathematics from King Abdullah University of Science and Technology (KAUST), in Saudi Arabia, and a PhD in Computational and Mathematical Engineering from ICME at Stanford. She met Margot at Stanford and has been contributing to WiDS for many years at conferences, workshops and datathons.After answering some questions about herself, Cindy stepped right into her co-host role to interview Margot. A native of the Netherlands, Margot said her career path was similar to Cindy’s as she started in math, got excited about applied math, and decided to study fluid mechanics. After getting her PhD at Stanford, she became a professor at the University of Auckland in New Zealand and then returned to Stanford where she has been a professor for 20 years. During this time, she has been an accomplished researcher, professor, mentor, and leader in the School of Earth, Energy & Environmental Sciences, the Institute for Computational & Mathematical Engineering (ICME), and Women in Data Science (WiDS).When asked how she managed to juggle all of these things, Margot said she learned to not worry about making mistakes or striving for perfection, saying, “80% is perfect”, adding “I always felt I can't have it all. So you make choices, and there's always something that's got to give.” Cindy agreed that the busier she is, the better she manages her time, and when you have many balls in the air, often what you learn in one area can help you solve problems in another. In discussing the “imposter syndrome”, Margot said she had often felt like an imposter, and soon discovered this was a common feeling among students and faculty at Stanford. And it’s even stronger when you stand out, like a woman in STEM. It puts an extra burden on you to succeed to set the example for those who come after you. The pace of research in AI and deep learning contributes to feeling like an imposter. People publish very quickly and it's hard to understand what really good solid research is and what is just an idea. It gives people this sense that they're not on top. They forget the purpose of school is creating a lifelong interest in learning. “There's a lot of failure on the way to success. My favorite definition of an expert is somebody who's made every possible mistake.”RELATED LINKSConnect with Cindy Orozco on LinkedIN Find out more about Cerebras SystemsConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

When Tahu Kukutai’s father went to school, he wasn’t allowed to speak his native language. If he did, he would be hit by his teachers. While the situation for the Maori people in Aotearoa (the Maori name for New Zealand) has improved somewhat since then, Tahu has dedicated her career to advocating for the rights of Indigenous peoples to preserve their native language, identity, communities, and culture. In today’s world, power over data is a central component of indigenous self-determination. Government agencies decide what data gets collected on whom and how it gets used, shared, and stored. There's a long history of data colonialism and state surveillance of indigenous communities designed for government priorities. Indigenous data sovereignty provides a framework to determine what data are collected and how it’s used, the ethical framework and governance, and the intended beneficiaries. Indigenous peoples have a different perspective on data. Most western frameworks about data protection, rights, and privacy are focused on the individual. The indigenous concept of data, data sovereignty and data rights are instead focused on the collective. Indigenous data are any information that impacts both the individual and the collective such as natural resources, nations, peoples, traditional and cultural information, oral histories, and ancestral knowledge. Tahu is advocating for indigenous data sovereignty as a founding member of the Māori Data Sovereignty Network Te Mana Raraunga and the Global Indigenous Data Alliance (GIDA) that works to control the collection, ownership, and application of data about their people, territories, and natural resources.While there's a growing willingness from government agencies to engage in conversations around Maori data sovereignty, she says there is a lot of work ahead. Private sector big data organizations also have a major role to play, and she hopes they will be more proactive about doing this work internally to be relevant to the growing Maori population. Maori currently make up 16-18% of the Aotearoa population, compared to roughly 5% indigenous populations in the US, Canada, and Australia. Tahu says that advocating for any issue fundamental to the wellbeing of the Maori, whether it’s language revitalization, land rights, or water rights, has always been driven by the Maori. “I have huge optimism that change can occur, but it will only ever be driven by ourselves. That doesn't mean that others don't have a role to play and there are some very positive steps,” she says. “There's incredible resilience, fortitude, and tenacity in indigenous communities around the world who have refused to let go of their identity. The challenge is to move from surviving to thriving.”RELATED LINKSConnect with Tahu on LinkedIN and TwitterFind out more about National Institute of Demographic and Economic Analysis (NIDEA)Find out more about University of Waikato Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Allison Koenecke, who received her PhD from Stanford’s Institute for Computational and Mathematical Engineering (ICME), describes how her experiences in academia and industry shaped her decision to return to academia. Currently a postdoc at Microsoft Research in the Machine Learning and Statistics group, she starts as an Assistant Professor of Information Science at Cornell University next year. Her research interests lie at the intersection of economics and computer science, with projects focusing on fairness in algorithmic systems and causal inference in public health.​Allison says in her career so far, she has always tried to keep as many doors open as possible but recognized, at some point, you have to start closing doors and specialize. After getting her bachelor’s degree in mathematics from MIT, she worked in economic consulting for a few years and realized she wanted to do something with more social benefit. While she was working in industry and during summer internships, she kept in touch with professors and kept up with her research so she could have that option open if she wanted to go back to school. One of the main reasons she chose to stay in academia was industry and government did not offer what she was looking for. For example, if you stay in industry long-term and your research is critiquing big tech companies, you may run into roadblocks or not be seen as a neutral third-party observer, as you would be seen in academia. Or at a government think tank, your work wouldn't necessarily have as much reach as in academia. But even more, a lot of the reason she stayed in academia was the people. Allison’s research is interdisciplinary and falls into two categories. The first is a fairness in online services and algorithmic services, such as speech-to-text or online ads and looking at the racial disparities in those services. And the second branch is on causal inference, which is usually applied to things like public health. Most of her thesis focuses on fairness with the services that we use every day.One of her research projects is about Google ads used to enroll people in food stamps and how to make decisions about fairness when it costs more to show those ads to Spanish speakers versus English speakers. She is also doing fairness research on racial disparities on speech-to-text systems developed by large tech companies to ensure systems are usable for African American populations that may not able to use their tools simply because they speak with a different variety of English than standard English. She says you need to have people thinking about fairness problems at all steps of the pipeline before you build a product that might harm certain groups of people. She’s hoping to bring awareness to different blind spots to make sure technology actually works for everyone.RELATED LINKSConnect with Allison on LinkedIN and TwitterFind out more about the Microsoft Research Machine Learning and Statistics groupFind out more about Cornell University Information ScienceConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Though Karina showed an early aptitude in math, her high school counselor advised her against pursuing an engineering degree. She ignored his advice and went on to earn her undergrad degree in mechanical engineering from the University of Rhode Island and a PhD in Aeronautics from Caltech. She landed her first job as a speech-to-text engineer at TRW where she was awarded her first patent. She then moved on to technology transfer as a patent agent at the Jet Propulsion Lab. She bounced back to academia, managing corporate partnerships for Caltech, and then returned to industry as Google’s University Lead for Google Cloud AI/Machine Learning. She is now at SAP as Global Head of Academies and University Alliances, continuing to connect industry and academia.In her diverse career spanning business and education, she has seen increasing power concentrated in big tech companies through their ownership of immense datasets and computational power. Companies are also attracting talent away from universities that are now having a hard time hiring enough computer science faculty. She says there are some creative ways to bring back some balance by companies hosting visiting faculty and industry partners coming in to teach at universities.Karina is also very concerned about ensuring fairness in data science. She explains that it’s not just the software that's being developed, but the datasets that are used to create predictive models. If a company just collects data from one demographic and then applies it to everyone, that introduces bias, and then the algorithms amplify these biases. She believes that the only way to address this is to have more ethnic, gender and geographic diversity in the field of data science. She sees a vital need to encourage more women and minorities to enter the field to bring diverse perspectives to data science.For people interested in pursuing a career in data science, she advises gaining the basic skills in math, science, and programming languages, but the most important quality is the ability to learn because everything is constantly changing. She recommends keeping your options open, acquiring as many skills as possible, and sharpening your interpersonal skills. Karina also says to challenge yourself. “We don't grow in a space of comfort. You grow when you're challenged, it's okay to be uncomfortable because that's likely the place of greatest growth. There’s no such thing as failure, you either win or you learn.”  RELATED LINKSConnect with Karina on LinkedIn and TwitterFind out more about SAPConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Fatima Abu Salem grew up in Lebanon and has focused her data science research on addressing critical challenges in the region, including problems around the Syrian refugee crisis, the water quality in Lebanon and their irrigation requirements for farmers. Fatima explains that her life journey is really enshrined in the conflict of the region. She was born to a Lebanese mother and a Palestinian father who had refugee status so she inherited this status. This meant there were many restrictions on what career she could pursue and her dream to become a doctor was out of reach. But she was good in math so majored in mathematics at the American University of Beirut and one of the only jobs available to her as a Palestinian was able to become a high school teacher. She taught for a few years but realized it wasn’t what she wanted to do with her life. At the time, she was working on her master’s thesis in Algorithmic Number Theory at the American University in Beirut and decided she wanted to pursue a PhD. She emailed Oxford’s Computing Lab and a prominent professor there, Richard Brent, wrote back and invited her come study there. While at Oxford, she studied computer science, building on her knowledge of mathematics. After Oxford, she returned to AUB in Lebanon in the fall of 2004 as a faculty member.Data science was gathering momentum, so she started learning about it and was especially excited about applying data science for the public good. Fatima is currently using data science to help out with some of the most urgent and critical questions in Lebanon and in the Middle East. She focused on what mattered to her most about the livelihood of people such as the plight of refugees, fake news, the demand on the power grid and infrastructure, food security and healthcare. She wanted to help empower the Lebanese institutions to learn how to make better predictions so they can be more prepared than when they were taken by surprise by a million and a half refugees coming to Lebanon.She has built collaborations with experts from the medical, agriculture, and public health schools for different research projects for social impact. One is trying to predict an irrigation metric to help farmers optimize their water usage so they can increase crop yields and reduce power consumption. Another project is trying to predict dementia with a public health school and a third project trying to predict birth defects using data using air pollution data.In Fatima’s talk at the 2021 WiDS Conference, Doing Data Science in Data Deserts, she discusses these projects and others such as quantifying anti-refugee bias across Lebanese news corporations, fake news detection, predicting primary health care demand by Syrian refugees in Lebanon, and understanding Syrian refugee mobility in Turkey. RELATED LINKSConnect with Fatima on LinkedIn and TwitterFind out more about American University of Beirut (AUB)Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Louvere Walker-Hannon has worked at MathWorks (the company that makes MATLAB) for over 21 years, where she’s also a STEM Ambassador. She studied biomedical engineering as an undergraduate at Boston University and did graduate work at Northeastern University in geographic information technology with a specialization in remote sensing.She loved working with MATLAB as an undergraduate and when MathWorks came to the career fair when she graduated, she sought them out, got an interview, and has been working there ever since.She says there are both technical and non-technical skills that are valuable in the field of data science. Technical skills include coding, some programming, a foundation in mathematics, some statistics, and in some cases physics. Non-technical skills are also very important. It’s critical to be able to communicate your findings clearly using a variety of techniques. She says stay away from technical jargon and communicate as if you’re having a conversation. A second important skill is active listening, to be open to suggestions from others, especially those who are new to the field.She explains that there are also barriers to people entering the field of data science. For some people, coding is a barrier to engagement with data science as many people in STEM professions are not comfortable with coding. MathWorks is doing more development to provide user interfaces or apps to give people a starting point without having to rely on writing code.There are also concerns about model interpretability where it’s difficult to get insights into how certain models work. More people are gaining awareness about the topic, and that's leading them to explore how to implement it and ask why it’s important. She is noticing that more people are trying to incorporate model interpretability into their data science applications.One of the systemic barriers is implicit bias. People are used to working with and being around people with certain characteristics. And in a work setting, there could be a project coming up, and there are several individuals who could work on this project. Many times, the people selected to work on a project tend to be the same individuals. But then it begs the question, when do others get the chance? There’s still a lot of under-representation from various population groups in data science. Even if people from an under-represented group have the skills and education, if they don’t feel like they belong, that can impact their productivity. It’s important to build a sense of community and have someone guide the person, make them feel welcome and help them become a part of the culture, so they can understand what they can do in order to thrive. Louvere is also a STEM ambassador at MathWorks where she volunteers in STEM advocacy and outreach in schools or on STEM panels. She loves hearing high school age and younger students at science fairs talk about their projects and see how proud they are of their work. This gives her hope that young people are excited about data, about analysis, and communicating their insights to others. RELATED LINKSConnect with Louvere on LinkedIn and TwitterFind out more about MathworksConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

After she earned her BA, MA, and PhD in economics from the University of Maryland, Menglin Cao spent six years at Fannie Mae before joining Wells Fargo. Over the past 15 years, she has seen a major shift in how financial institutions use data to drive business decisions. In the past, many business decisions were based upon the experience and judgement of senior executives, but today every decision must be backed up by data and analytics. Many aspects of the financial services that leverage data are great candidates for AI and ML models, such as customer experience, revenue generation, and risk management. She wants to ensure they build models that are fair, explainable, interpretable, and able to generate value.She offers several examples about best practices for success.To be useful, the data must first be focused on a foundational business question. “Data can tell many stories, but until you bring the story to the businesses and they can relate the story to a question, experience or concern that they have, it’s not going to lead to an actionable insight.” The journey to find that answer requires data science, data algorithms, operational procedures, implementations, and integrations of systems, but without the right question to begin with, it doesn’t lead anywhere. It’s also important to build a solution in a matrix organization rather than top-down approach, working with the technology team, model developers, subject matter experts, the model governance and validation teams. And data management has become centralized, shifting from silos to a central enterprise data lake where every line of business and all models depend on one source of truth. This removes the redundancies and duplications that silo-based data warehouse may create.To ensure that models are fair, they continually test them for bias. One way to make sure that these data are representative of the entire population is to conduct a sensitivity analysis. They can see if the model can stand on its own by “shocking it” in different ways to find any vulnerabilities that need to be addressed.She acknowledges that there are not yet many women leaders in fintech and the financial sector but she is hopeful that can change if women are given the opportunity. “I am in the position that I am in because my leaders trusted me and gave me a chance.” She hopes that leaders will continue to give diverse people a chance to grow and demonstrate what they can do. And then once the opportunity's given to you, then the rest depends on what you make out of it. RELATED LINKSConnect with Menglin Cao on LinkedINConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Karen Hao trained as a mechanical engineer and then joined a Silicon Valley startup, thinking that technology was the best means to create social change. While surrounded by smart people who were also passionate about using technology for social change, she soon discovered there were no incentives or pathways to accomplish this. “When you're inside a technology company and you're thinking this is going to help change the world, you're often blind to unattended consequences of your work,” she says.She decided to transition to a career in journalism where she could create social change by raising awareness about social impacts of technologies like AI, and how big tech companies engage in “ethics washing” to protect their profits. She is intrigued by the way that incentives shape the work that is done at a systemic level. She says every tech giant suffers from issues at the systemic level where there are people who deeply care about ethics within the organization, but it doesn't mean the company is willing to change the way their profitable technologies work. And employees are disincentivized to do this work because they could be fired.A high-profile example was the ethical AI team at Google was doing great work critiquing some of Google’s practices and tried to get the paper published. Google refused to let them publish it, censored their research and then fired both of the team leads. It later came out that this was just one instance of academic censorship, but Google had told many other researchers to strike a positive tone when talking about technology being developed by Google.For her article, How Facebook Got Addicted to Spreading Misinformation, she did a nine-month investigation into Facebook's responsible AI team that was supposed to be understanding and mitigating the unintended consequences of Facebook's algorithms. She found the team was focused on specific unintended consequences that are good for Facebook's growth like AI bias. It completely ignored the most important harms of Facebook's algorithms—misinformation, amplification, polarization, exacerbation, especially in the wake of the January 6 capital riots—because addressing them would undermine Facebook's growth. There have been times when Facebook was not only ignoring or negligent of the issues that its algorithms might be causing, but also purposely undermining some of the efforts to try and fix it because of this tension with the company's growth.She was glad to see policymakers cite her article at a recent congressional hearing, and hopes Congress has the political will to regulate companies like Facebook. She says it’s also important for every new generation of Facebook employees to become educated about these issues so they will hold the company accountable. She thinks AI research has shifted a bit over the last five years to be more focused on taking responsibility for societal impacts and part of that evolution is being driven by people on the inside who raised awareness and advocated for change. One of Karen’s inspirations to go into journalism was Rachel Carson's book Silent Spring that sparked a widespread environmental movement. She strives to write stories that activate that same level of change, transforming both the cultural discussions and policy around important issues.RELATED LINKSConnect with Karen Hao on LinkedIN and TwitterFind out more about Karen on her websiteFind out more about MIT Technology ReviewConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile