Women in Data Science Worldwide

Summary:Listen to the incredible and inspiring journey of Avalon Baldwin’s career journey. A self-described data nerd, she was not only the first in her family to attend college, she went on to get a graduate degree. Today she is an entrepreneur running her own consulting company. In conversation with Chisoo Lyons, Avalon shares how curiosity, mentorship, and coaching made a difference in her life. Highlights: (06:18): Exploring factors like how data is collected, the intention behind collecting a specific data point instead of another one, and how they can influence analysis and interpretation.(08:20): Working with students as individuals and promoting self-agency, as able to influence their own future. (12:02): Avalon describes her journey to become the first in her family to be a college student(32:02): Advice on finding a mentor. About the Guest:Avalon Baldwin master's degree in positive developmental psychology and evaluation from the Claremont Graduate University. She received her bachelor's degree in biopsychology from Mills College,. Avalon's consulting company, which she just recently launched, is called Curious Evaluation. Avalon provides consulting services for nonprofit organizations to help in evaluating the impact of their programs using data and science by framing the effort around the organization's mission, goals and values.Mentions:Connect with Avalon on LinkedIn Related Links:Curious Evaluation Connect with Us:Chisoo 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

In this episode, Margot Gerittsen speaks with Kim Grauer. Kim is the Director of Research at Chainalysis, where she examines trends in cryptocurrency economics and crime. Listen as she talks about her obsession with fighting fraud in the cryptocurrency market.Highlights:What is crypto crimeTrust in stable coinMisconceptions around cryptocurrencyUsing data and data science in fighting fraud About the Guest:Kim is the Director of Research at Chainalysis, where she examines trends in cryptocurrency economics and crime. She was trained in economics at the London School of Economics and in politics at Oxford University. Previously, she explored technological advancements in developing countries as an academic research associate at the London School of Economics and was an economics researcher at the New York City Economic Development Corporation. Related Links:ChainalysisNew York City Economic Development Corporation Connect with UsMargot Gerritsen 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

Michelle Katics, CEO  and co-founder of  BankersLab, discusses her journey in risk management training and the importance of integrating technical skills with business and soft skills. She shares her experience in helping banks navigate complex regulations and the need for training to improve understanding and decision-making. Katics emphasizes the importance of storytelling and simplifying complex concepts to effectively communicate with stakeholders. She also highlights the need for women to participate in data science and entrepreneurship, and encourages everyone to continue learning and collaborating to drive innovation and growth. Katics also discusses her involvement in volunteer work, including supporting migrants and refugees and mentoring aspiring entrepreneurs. She concludes by encouraging listeners to embrace diverse skill sets and collaborate to achieve better outcomes.Highlights:Why Michelle went into risk management and why it’s so critical for enterprise success (00:58)Blending business and soft skills with technical skills for optimal outcomes (04:52)Importance of storytelling (07:19)Mentions:Connect with Michelle Katics on LinkedInBios:Michelle Katics is the co-founder and CEO of BankersLab. BankersLab provides a virtual simulation platform taking learning to the next level, combining business expertise in lending with numerical simulation and gamification. Michelle is a thought leader in the fintech revolution and a champion of talent transformation and innovation. During her career she worked at the Federal Reserve Bank of Chicago, the International Monetary Fund, Fair Isaac, and with numerous financial institutions who were her clients in over 30 countries. Alongside her impressive career accomplishments, she has a diverse and rich portfolio of volunteering activities being in service of others.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 Michelle on Using storytelling to communicate with stakeholders.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

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

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