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Leading women in data science share their work, advice, and lessons learned along the way. Hear how data science is being applied and having impact across domains— from healthcare to finance to climate change and more. Hosted by Professor Emerita Margot Gerritsen from Stanford University and Chisoo Lyons, Chief Program Director of Women in Data Science Worldwide.
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.
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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.
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Chisoo Lyons on LinkedIn
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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 NOTES
In This Episode We Discuss:
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Connect with Kate Kolich on LinkedIn
Find out more about the Reserve Bank of New Zealand
View the EECA’s New Zealand Energy Scenarios Data Visualization
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Follow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)
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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 LINKS
Connect with Telle Whitney on LinkedIn
Find out more about AnitaB.org
Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn
Follow 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 LINKS
Connect with Srujana Kaddevarmuth on LinkedIn
Find out more aboutWalmart
Learn more about the United Nations Association San Francisco Chapter
Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn
Follow 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 LINKS
Connect with Veronica Edwards on LinkedIn
Find out more about Polygence
Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn
Follow 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 LINKS
Connect with Priya on LinkedIN
Find out more about the Climate Change AI
Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn
Find 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 LINKS
Connect with Lesly on LinkedIN
Find out more about the Universidad Privada Boliviana
Connect 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 LINKS
Connect with Leda on LinkedIn or Twitter
Find out more about Systematica Investments
Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn
Find 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 LINKS
Connect with Jessica on LinkedIN
Find out more about the University of Adelaide
Connect with Cindy Orozco Bohorquez on LinkedIN
Amanda is a wife. A mother. A blogger. A Christian.
A charming, beautiful, bubbly, young woman who lives life to the fullest.
But Amanda is dying, with a secret she doesn’t want anyone to know.
She starts a blog detailing her cancer journey, and becomes an inspiration, touching and
captivating her local community as well as followers all over the world.
Until one day investigative producer Nancy gets an anonymous tip telling her to look at Amanda’s
blog, setting Nancy on an unimaginable road to uncover Amanda’s secret.
Award winning journalist Charlie Webster explores this unbelievable and bizarre, but
all-too-real tale, of a woman from San Jose, California whose secret ripped a family apart and
left a community in shock.
Scamanda is the true story of a woman whose own words held the key to her secret.
New episodes every Monday.
Follow Scamanda on Apple Podcasts, Spotify, or wherever you listen.
Amanda’s blog posts are read by actor Kendall Horn.
