Women in Data Science Puget Sound 2026 Conference

Aashreen Raorane is a Senior Data Scientist specializing in analytics, machine learning, and cross-functional decision support. She holds a Master’s in Computer Science from the University of Southern California, where she focused on data science and applied ML. Her work centers on bringing clarity to complex problems and influencing strategic direction through practical, well-framed questions. She is passionate about sharing skills that help others think more clearly and lead more effectively in their roles.

Akriti Chadda is an applied machine learning scientist specializing in search, relevance and generative AI systems deployed at scale. Her work focuses on the full lifecycle of AI, from modeling and experimentation to production deployment, monitoring and long-term system reliability. In recent years, she has been deeply involved in agentic and generative AI systems, where non-determinism and autonomy introduce new technical and leadership challenges.

Beyond technical execution, Akriti is passionate about communication, mentorship and helping data professionals grow into thoughtful leaders. She frequently speaks about operating complex AI systems responsibly, aligning stakeholders around uncertainty and translating advanced ML concepts into practical, real-world impact.

Alisha Gala is a Senior Data Scientist at Microsoft with over eight years of experience across software engineering, applied data science, and AI-driven experimentation. Her work focuses on driving engagement and growth across Windows through metric design, large-scale experimentation, personalization and recommendation systems, and cohort-based behavioral analysis. Alisha partners closely with product and engineering teams to translate complex data into clear, decision-ready insights. She is a frequent speaker in technical and cross-functional forums, known for turning analysis into narratives that directly inform product strategy.

I am a multidisciplinary Dr.-Ing. with 12+ years bridging mechanical engineering and data science while bringing applied research to industry. My career focuses on solving complex industrial challenges through innovative approaches combining domain expertise with cutting-edge technology.

After completing my Bachelor's in Mechanical Engineering, I pursued Applied Mechanics in Sweden, a premier program for industrial demands. This led to five years of industry experience, where I identified critical gaps in automotive R&D data management and optimization, developing solutions that shaped my research direction.

At Zeekr, I pioneered the first fully automated semantic-based reporting system for crash simulation data, making previously inaccessible results centralized and comparable. This breakthrough led to collaboration with Fraunhofer Research Institute for doctoral research on knowledge graphs for automotive crash simulation, establishing unique industry-academia partnership.

Following graduation, I expanded expertise in graph-based machine learning through consultancy across diverse domains: contract management at eccenca GmbH, job profiling at Interim, medical applications at Merck, and technical documentation at Zeiss SMT. This demonstrated versatility of graph-based approaches in solving complex data challenges.

Currently at Amazon, I've returned to mechanical engineering roots, leading initiatives leveraging graph technologies for reliability and maintenance challenges to enable Digital Twin. My work synthesizes traditional engineering principles with modern AI methodologies, positioning me at the forefront of next-generation industrial solutions.

Research interests include knowledge graphs, agentic reasoning, CAE simulation, graph analytics, LLM, and AI applications to technical domains. I'm passionate about translating academic research into practical industrial applications driving real-world impact.

I am a Senior Data Scientist at the Pacific Northwest National Laboratory (PNNL) specialized in processing and modeling energy and Earth system data for impactful decision-making science. At PNNL, I am the lead architect of novel GeoAI data pipelines and manage several AI-driven and/or cloud-native applications for U.S. energy and environmental mission areas. This includes six research, agentic, and generative AI applications to streamline federal permitting reviews, a techno-economic simulator for advanced geothermal systems (GeoCLUSTER), and a U.S. energy feasibility mapper (GRIDCERF). Combining data science, computational modeling, and environmental science, I am also deft in geographic information systems (GIS) and statistical modeling used to better enable intelligent mapping and environmental monitoring analyses. My leadership has guided data product teams to deliver impact and value to sponsors across the Department of Energy, including earning project-level recognition by the White House in the “AI for Good” space and in “America’s AI Action Plan”.

I am from Kolkata and have a strong academic foundation in mathematics and applied sciences. I completed my undergraduate and master’s degrees in Mathematics from Banaras Hindu University, followed by an M.Tech in Cryptology and Security from the Indian Statistical Institute, Kolkata. In 2023, I was selected for a six-month internship at Amazon, where I worked on applied machine learning problems at scale. In January 2024, I joined Amazon as a full-time Data Scientist in the AFT organization and subsequently transitioned to the FinAuto team. My current work focuses on building production-grade data science and Generative AI systems, including taxonomy-agnostic classification and supplier-aware recommendation solutions for enterprise procurement. Besides work, I love to cook, dance, and spend time with animals.

Booma Sowkarthiga Balasubramani is a Senior Data Scientist at Microsoft with deep expertise in AI and Data Science, built over a decade across academia, research, and large-scale industry applications. Holding a Ph.D. in Computer Science (Data Science) from the University of Illinois at Chicago, Booma has developed frameworks for ontology engineering, ontology matching, predictive modeling, and geospatial data analytics. At Microsoft, Booma leads work involving Copilot on Taskbar, predictive modeling, metric design, experimentation, and data-driven growth strategies for Windows. A seasoned speaker and educator, Booma has delivered talks at global conferences, featured sessions at developer events as well as academic events, and taught university courses, consistently making complex AI and Data Science concepts accessible to diverse audiences.

Catherine Nelson is an experienced data scientist and ML engineer, and the author of two O'Reilly books: Software Engineering for Data Scientists (2024) and Building Machine Learning Pipelines (2020). Previously, she was a Principal Data Scientist at SAP Concur, where she deployed NLP models to production and created innovative features including ML-powered carbon emissions analytics. She is currently consulting for startups on AI evaluation and developer relations. Catherine holds a PhD in Geophysics from Durham University and a Masters in Earth Sciences from Oxford University.

Emma Rosenthal is a Data Scientist at Expedia Group where she works on the Checkout team, focusing on AI Driven Insights, AI integrations into the checkout flow with ChatGPT, A/B testing, and data-driven product optimization. Prior to Expedia, Emma received her Master’s in Computer Science and Bachelor’s of Economics from the University of Chicago.

I am a data scientist pursuing a career at the intersection of computing, biology, and sustainability. My experience includes working at biotech companies like Amyris (engineer yeast to convert sugar into alternatives to petroleum based products) and LanzaTech (convert carbon emissions to ethanol with bacteria), and completing a PhD in the Computer Science program at UW (using ML techniques to model DNA patterns in methane-eating bacteria). When I'm not nerding out about climate biotech, you can find me enjoying fresh air on PNW trails or rolling dice to explore D&D fantasy realms.

Erin Zionce is a Data Scientist at the Pacific Northwest National Laboratory with a background in fisheries ecology. Her research contributes to juvenile and adult fish passage studies by integrating ecological expertise with data science through statistical modeling, machine learning, and computational tools to support environmental science and hydropower systems management.

Strategic leader with 17+ years experience in product and tech operations and strategy. Currently the Chief of Staff for Data & AI at Expedia Group. Pervious experience includes Google, Vivint Smart Home, Ancestry.com and The New York Times.

Harsheeta Venkoba Rao is a Founding software engineer at Gone.com with extensive experience in agentic AI, machine learning, and building reliable end-to-end software systems. She holds a master’s degree in Electrical and Computer Engineering, specializing in machine learning and data science.

I am a civil engineer and data scientist with six years of professional engineering experience, three years of Data Science, and eight years of academic research focused on predictive modeling of complex dynamic systems. I hold a PhD in Civil Engineering, where my research applied system identification, time-series analysis, and state-space modeling to large-scale experimental data to study the seismic response of foundations and support infrastructure resilience. My work has been published in peer-reviewed journals and presented at international conferences and workshops.

Following my doctorate, I worked at Shannon & Wilson, a leading geotechnical consulting firm in Seattle, contributing to high-impact projects in the Pacific Northwest and San Francisco Bay Area, including seismic resilience analyses and large-scale numerical simulations for critical infrastructure. I later transitioned into data science, completing advanced training in computer science, machine learning, deep learning, and AI. I currently work as a data scientist in higher education, applying predictive modeling to student success and retention initiatives.

Data engineering leader with 15+ years of experience building data platforms and helping teams make better decisions. Currently a Principal Data Engineer at JumpCloud. Previously worked at OfferUp, Disney, and Visa, leading large-scale data and analytics initiatives across cloud environments.

Jennifer Hay is a career coach and resume writer specializing in technology, data, and analytics careers. As the founder of Tech Career Services and IT Resume Service, she combines technical expertise with career development experience to help clients define goals, create actionable plans, and present their strengths with confidence. She uses a proprietary career assessment and planning methodology (STIK) to guide students, recent graduates, and mid-career professionals in navigating the tech job market. Jennifer is certified in IT Resume Writing (CRS+IT), Student Career Coaching (CSCC), and holds CBIP credentials in Data Analysis and Business Analytics.

Sr Data Scientist, Data Engineer, and ML Engineer with 8+ years of experience building production ML systems and data infrastructure. At Nordstrom, co-launched Smart Markdown—the company's first dynamic pricing optimization model—while designing Nordycast, an ML Ops platform widely adopted across the org. Spoke about the platform at WiDS Puget Sound 2022. At Textio, built production NLP systems including BERT-based discrimination detection and led cross-functional data migrations and experimentation frameworks. Currently an AI/LLM Engineer at a stealth-mode startup. Passionate about designing systems people adopt and elevating teams through mentorship and technical standards.

Jingyi Du is a Principal Data Science Manager with over a decade of experience spanning software engineering and applied data science. Jingyi leads Windows engagement strategy and conduct large-scale experimentation at Microsoft, driving growth through data-driven decision frameworks. With a background in Computer Science and Decision Science, Jingyi has authored research presented at Microsoft ML & Data Science Conference and delivered 50+ talks to audiences from technical teams to senior leadership.

Kristin is an associate director at Pfizer, where she leads a team of programmers building data pipelines to automate biomarker data quality control. This work improves data accessibility and accelerates decision‑making in clinical trials. She is passionate about data standardization and about preparing complex, often inconsistent data for meaningful analysis. Kristin aims to create an environment where data‑driven decision making can thrive and to bridge the gap between raw data and the scientists eager to translate it into breakthroughs. Outside of work, she enjoys gardening, ceramics, painting, reading, and board games.

Nandita Krishnan is a Consultant-turned-Data Scientist who brings a unique blend of strategic thinking and technical expertise to her work. Currently part of Adobe's team, she focuses on enhancing user experience for flagship products such as Premiere Pro by uncovering user needs hidden within complex data.

Beyond her day-to-day role, Nandita is deeply curious about the evolving tech landscape and is constantly exploring and experimenting with the latest tools and technologies, expanding her skill-set and staying at the forefront of data science innovation.

Passionate about creating pathways for others, Nandita is also an active advocate for women in STEM. She regularly mentors aspiring Data Scientists and participates in speaking engagements to inspire the next generation in tech

I'm a Principal Applied AI Architect at AWS with 17+ years of experience in architecture, including 10+ years focused on cloud and AI. As a thought leader in AI-driven solutions, I regularly speak at global tech conferences including AWS re:Invent, AWS re:Inforce, AWS Summits, NRF, and Grace Hopper Celebration (GHC), where I share insights on bridging the gap between AI and real-world business applications.

My expertise spans cloud architecture, generative AI, and retail innovation, making me a recognized voice in the industry. I'm the author of the published book AWS Solutions Architect Associate Certification Guide and a contributor to the Responsible AI Lens for the AWS Well-Architected Framework.

My work has been recognized with several prestigious awards, including the Advancing Women in Technology (AWT) 2023 Rising Stars Award, Success Quarterly 2025, the Globee Award for thought leadership in artificial intelligence, and the Global Recognition Award as a standout leader in the industry.

I'm passionate about helping organizations unlock the transformative potential of AI through practical, scalable solutions—from property inspection and document processing to customer experience enhancement and workforce productivity. I've successfully identified and prioritized AI use cases across diverse industries, including finance and real estate.

I have been doing AI and ML-modeling for Xbox for the past 4 years as a Data Scientist. My work helps create better marketing, finance, business planning decisions, and has also helped with Xbox's sustainability goals! I studied Data Science at UC Berkeley and enjoy skiing, tennis, yoga and biking.

Rachel Wagner-Kaiser has 15 years of experience in data and AI, entering the data science field after completing her PhD in astronomy. She specializes in building NLP and AI solutions for real-world problems constrained by limited or messy data. Rachel leads technical teams to design, build, deploy, and maintain NLP solutions, and her expertise has helped companies organize and decode their unstructured data to solve a variety of business problems and drive value through automation. Rachel is also the author of the recent book "Teaching Computers to Read" (http://amazon.com/dp/1032484357) and corresponding code companion.

Sandra Rech is an Earth Scientist at Pacific Northwest National Laboratory, where she contributes to fish telemetry projects to study salmonid migration through dams. She has a background in computer science, mathematics, and oceanography, with previous work in mathematical modeling, oyster restoration, and ecological data management. She is passionate about integrating ecology and data science to address complex environmental challenges.

Erin Zionce is a Data Scientist at the Pacific Northwest National Laboratory with a background in fisheries ecology. Her research contributes to juvenile and adult fish passage studies by integrating ecological expertise with data science through statistical modeling, machine learning, and computational tools to support environmental science and hydropower systems management.

Shaili Guru is an AI product leader and educator with 10+ years of experience building AI products at Amazon, Disney, Nike, and T-Mobile. She currently teaches AI Product Management at the University of Washington's Global Innovation Exchange and runs Bluenox.ai, helping organizations and product teams adopt AI effectively. Her Substack newsletter, AI Product Management Guru, is read by over 4,000 PMs worldwide. Shaili holds a Technology Management MBA from the UW Foster School of Business and a BS in Biology from Baldwin-Wallace University.

Ph.D. in Machine Learning with 5+ years of industry experience working in high-performance, worldwide scale projects on fraud detection, warehouse management & promotion targeting across fintech, e-commerce & healthcare. She is skilled in supervised & unsupervised machine learning algorithms, building end-to-end ML pipelines, applied statistics, Python & SQL.

She has presented her research at various academic and practitioner conferences like Grace Hopper Celebrations (India), the Women in Machine Learning workshop at NeurIPS & ICML, and ACM Conference on Machine Learning and Human-Computer Interaction (2020). She has served as a visiting faculty for courses on AI, ML, and business analytics across management institutes in India.

Sneha Sivakumar is a Product leader at Amazon, where she leads Content Risk Moderation (CRM) for the self-publishing books business, Kindle Direct Publishing. Prior to Amazon, she worked at KPMG where she led the Technology Risk practice advising large public companies on mechanisms to quantify and mitigate technology, financial and social risk. She is experienced in building consumer and enterprise products that help organizations manage risk through the use of ML, automation and human inputs. Sneha holds a BS in Engineering from Anna University, India, an MS in Industrial and Systems Engineering from USC and an MBA from Kellogg School of Management.

Sridevi Wagle is a Machine Learning Engineer at Pacific Northwest National Laboratory with a master’s degree in Computational Science. She has experience developing AI and machine learning tools for extracting and analyzing information from large-scale, multimodal scientific data. Her work includes building systems for knowledge retrieval and semantic search using advanced language models and data integration techniques. Sridevi’s research interests include explainable AI, uncertainty quantification, and visualization methods to support data-driven decision-making in scientific domains.

Hello, I’m Swapnil. I was born and raised in India and moved to the United States in 2018. I earned my BTech from the Indian Institute of Technology, Delhi, and my master’s degree from Carnegie Mellon University in Pittsburgh.
I’ve built my career as a Data Scientist across diverse organizations. I began at a startup in Pittsburgh, then spent nearly three years at Lubrizol Corporation in Houston, Texas. Currently, I work as a Data Scientist at Microsoft, specializing in product data science.
Outside of work, I enjoy painting, reading, and cooking. I’m very outdoorsy and love cycling, kayaking, hiking, and camping. I also have a five-year-old German Shepherd who keeps life busy, active, and joyful.

Tiffany Dedeaux is a Master Certified Coach (MCC) and leadership development practitioner working at the intersection of organizational change, professional identity, and career transition. She is the founder of Sacred Time, where she partners with professionals navigating complexity, ambiguity, and evolving systems — including leaders and practitioners in technical and data-adjacent fields.

With over 15 years of experience, Tiffany supports individuals and groups as they step into influence, clarify their professional narratives, and lead through change with integrity. Her work prioritizes discernment, clear thinking, and sustainable leadership presence over performance for performance’s sake.

Tiffany holds a Master of Arts in Ecopsychology and Cultural Transformation, grounding her work in a systems-level understanding of how people, roles, and environments shape one another. In addition to her coaching practice, she has held senior volunteer and governance leadership roles within professional associations, leading through restructuring, crisis response, and strategic realignment.

She is a frequent speaker and facilitator for conferences and professional communities, including Women in Data Science events, PyData chapters, and career-focused organizations. Tiffany’s work resonates especially with women and gender-diverse professionals navigating transition, visibility, and influence in data-driven and technical environments.

I am a Data Engineer and Machine Learning professional with over 8 years of experience building high-impact data solutions for industry leaders such as Microsoft, Fred Hutch Cancer Center, and T-Mobile. Currently, I am pursuing my Masters in Data Science and Analytics at Georgia Tech, where I specialize in architecting unified data environments and productionalizing ML models for complex fields ranging from quantum computing to immunotherapy research.

Beyond my work with high-dimensional datasets, I am a passionate advocate for "Data Science for Social Good." I have volunteered my expertise as an NLP data scientist for the investigative news organization WhoWhatWhy and have conducted research for the Art of Living to scientifically validate the impact of meditation on stress reduction.

As a certified yoga instructor and a public school art volunteer, I pride myself on my ability to translate complex technical concepts into accessible, human-centric narratives. My goal is to use data not just for optimization, but as a tool to inspire action and drive meaningful social change.