Women in Data Science Worldwide

Highlights Bio Shir Meir Lador leads a team evangelizing applied AI at Google cloud. Previously, she worked as the AI group Manager of the Document Intelligence Group at Intuit, where she led teams in developing AI services that helped consumers and small businesses prosper. Prior to intuit, she worked at 2 Israeli startups as a data scientist and researcher. A recognized leader in AI and data science, Shir is a former WiDS Tel Aviv Ambassador, co-founder and organizer of PyData Tel Aviv, and co-host of Unsupervised, a podcast exploring the latest in data science. She frequently speaks at international AI and data science conferences, sharing insights on applied machine learning and AI innovation. Shir holds an M.Sc. in Electrical Engineering and Computers from Ben-Gurion University, specializing in machine learning and signal processing. Passionate about fostering inclusive data science communities, she actively contributes to initiatives that bridge AI research and business impact. Links and Resources Google Developer Workshop Connect with Shir Shir Meir Lador on Linkedin, Medium, and X Connect with Us Shelly Darnutzer on LinkedIn Follow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide) Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Highlights Bio Sohini Sarkar is a Principal Product Manager and a Senior Team Lead at MathWorks. Her career started with earning a Ph.D. in Civil Engineering and then working on DOE and EPA projects as a postdoctoral researcher. She has held various positions at Dassault Systèmes, ranging from a solution consultant to a market and competitive intelligence analyst, concurrently earning her MBA. Currently at MathWorks, Sohini focuses on established areas such as Math, Statistics, Optimization, and Machine Learning, to more emerging technologies such as Quantum Computing, Large Language Modeling and Generative AI, as well as Scientific Machine Learning. Connect with Sohini Sohini Sarkar on Linkedin Connect with Us Chisoo Lyons on LinkedIn Follow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide) Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Bio Claudia Perlich is Managing Director and Head of Strategic Data Science for Investment Management at Two Sigma, where she has worked for seven years. In this role, Claudia is responsible for developing innovative alpha strategies at the intersection of alternative data, thematic hypotheses and machine learning in public markets. Claudia joined Two Sigma from Dstillery, an AI ad targeting company, where she worked as Chief Scientist. Claudia began her career in data science at the IBM Watson Research Center, concentrating on research in data analytics and machine learning for complex real-world domains and applications. Since 2011, Claudia has served as an adjunct professor teaching Data Mining in the M.B.A. program at New York University’s Stern School of Business. Claudia received a Ph.D. in Information Systems from Stern School of Business, New York University, holds an M.S. of Computer Science from Colorado University and a B.S. in Computer Science from Technical University Darmstadt, Germany. Connect with Claudia Claudia Perlich on Linkedin Connect with Us Margot Gerritsen on LinkedIn Follow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide) Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Highlights Bio Maritza is an engineering manager at Klaviyo, where she leads a team focused on creating tools that help marketers better understand and optimize their campaign and flow performance. She enjoys collaborating with her team to develop solutions that provide clear, actionable insights for users. Before joining Klaviyo, Maritza worked at startups in the clinical trial management and natural language processing (NLP) sectors, where she gained experience in applying software to solve practical challenges. She began her career as a research assistant in a computational neuroimaging lab, where she was introduced to software development through the lab's open-source projects. This experience sparked her interest in using technology to address real-world problems. Outside of work, Maritza enjoys knitting, though much of her time is happily spent caring for her 1- and 3-year-olds. Connecting with Maritza Maritza Ebling on Linkedin Connect with Us Tina Tang on LinkedIn Follow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide) Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Bio Huda Nassar is a senior computer scientist at RelationalAI working on building the graph algorithms library offered as part of RelationalAI's product. Previously, Huda obtained a PhD in Computer Science from Purdue University and was a postdoc fellow at Stanford's School of Medicine. Huda is also known for her "Julia for Data Science" course which had over 13,000 students and focused on Data Science methods including graph analytics. Connect with Huda Huda Nassar on Linkedin Connect with Us Margot Gerritsen on LinkedIn Follow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide) Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Bio Hannah Pham is a seasoned data leader with experience building and scaling data teams at top tech companies like Airbnb and Pinterest. Hannah's expertise spans consumer and monetization domains. As the Head of Data Science for the consumer area at Pinterest, she leverages data to bring the best experience to Pinners and drive business growth. Hannah is also a successful startup founder with Skin AI, a personalized skincare company that she co-founded in 2018. Connect with Hannah Hannah Pham on Linkedin Connect with Us Chisoo Lyons on LinkedIn Follow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide) Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Bio Accomplished executive with P&L responsibilities with extensive expertise in product management, management consulting, strategy, marketing, and product development. Has successfully grown newly established businesses and product portfolios from conceptualization to successful go-to-market. As a strategic business and technical decision-maker, has a proven ability to turn around struggling portfolios and businesses to achieve consistent growth and profitability. Holds a strong track record in establishing and leading international, cross-functional teams with responsibilities in general management, client management, strategy development, and execution. Connect with Jiani Jiani Zhang on LinkedIn Connect with Us Tina Tang on LinkedIn Follow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide) Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Sydney Hazen, a Privacy Data Scientist at Ford, shares her journey from a college intern to a full-time role. She highlights how internships can lead to job offers and the importance of real-world experience and corporate navigation. Sydney emphasizes applying technical skills with a socially conscious approach, understanding problems before diving into data, and credits her professor and diverse classmates for shaping her perspective. She plans to pursue further education and explore different roles, underscoring the unique position of women in data science and the need for self-advocacy and finding mentors. Highlights Bio Sydney is a Privacy Data Scientist at Ford Motor Company, as a part of the Ford College Graduate (FCG) rotational program. She is a recent graduate of the University of Notre Dame, with a degree in Applied and Computational Mathematics and Statistics (ACMS) and a minor in Data Science. She has a range of experience spanning software development, data engineering, and analytics and is always looking to gain new knowledge in the technology sphere. Connect with Sydney Sydney Hazen on Linkedin Connect with Us Chisoo Lyons on LinkedIn Follow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide) Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Highlights Bio Karin Golde, is the Founder of West Valley AI. She helps businesses and technical leaders navigate the rapidly developing landscape of AI and Large Language Models by sharing her expertise which has ranged from executive leadership roles at multiple startups to heading the language engineering division for the AI Data team at Amazon Web Services. Her philosophy is to cut through the hype, collaborate with integrity, and keep a laser focus on providing value to your business. Connect with Karin Karin Golde on Linkedin Website West Valley IA Connect with Us Chisoo Lyons on 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

Highlights: About the Guest: Colleen Farrelly is an author and senior data scientist. Her research has focused on network science, topological data analysis, and geometry-based machine learning. She has a master's from the University of Miami and has experience in many fields, including healthcare, biotechnology, nuclear engineering, marketing, and education. Colleen wrote the book, The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R. Mentions: Connect with Colleen Farrelly on LinkedIn Related Links: The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R Connect with Us Margot Gerritsen on LinkedIn Listen and Subscribe to the WiDS Podcast on Apple Podcasts,Google Podcasts,Spotify,Stitcher

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 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

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: 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 Us Margot Gerritsen on 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

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: 04:5207:19Mentions: Connect with Michelle Katics on LinkedIn Bios: 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 Us Chisoo Lyons on 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

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: Mentions: Connect with Mary Krone on LinkedIn Bios: 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 Us Chisoo Lyons on 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

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: RELATED LINKS 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 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 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

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...

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...

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,...

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