PyLadies SF at Snowflake: Data Science in the Age of AI

Rose has over a decade of experience in causal inference and experimentation and holds a PhD in Economics from Stanford University. Her goal is to use data to drive business strategy, and she enjoys using AI to reinvent the data science job description. At Snowflake, she works on B2B platform products and brings extensive experience in consumer-focused data science from her time at LinkedIn. Kasia Rachuta, Senior Staff Data Scientist, Intuit Kasia is a Senior Staff Data Scientist at Intuit, where she works on payments and uses AI daily to sharpen how her team builds and decides. Her expertise spans identity verification, payments, sales analytics, ecommerce, and infrastructure. Previously, she was a Data Science Tech Lead at Square, and before that worked at Medium and FiveStars, where she ran numerous A/B tests to inform product decisions and contributed to company-wide experimentation initiatives. In her free time, she enjoys travel, scuba diving, and reading. Lilinoe Harbottle, Biomedical Engineering Researcher | Data Systems, VC-Backed Startup Leveraging a background in software delivery at Auris Health, Lilinoe engineers data infrastructure to advance healthcare technology. She specializes in implementing robust validation layers to ensure system trust and compliance in highly regulated environments. Tentative Agenda: 6:00 pm: Check-in + Networking 6:15 pm: Welcome and Community Announcements 6:45 pm: Rose Tan (25 min, 5 min Q&A) Title: How I AI: A Data Scientist's Perspective 6:55 pm: Kasia Rachuta (25 min, 5 min Q&A) Topic: The Data Scientist's Job in the Age of AI: What Changed, What Didn't 7:20 pm: Lilinoe Harbottle (30 min) Topic: Trust in the Loop: System Design and Data Integrity in Regulated Environments Description: In regulated fields like MedTech, unverified automation introduces severe operational liabilities. This session runs a practical post-mortem on a silent data pipeline failure to isolate where automated logic drifted from compliance requirements, offering a clear blueprint on how to build validation layers that protect production systems. 7:50 pm: Wrap-up + Networking 8:30 pm: Event ends Who should attend:If you’re navigating your analytics career alongside AI, then this event is for you. Attendees are data scientists, data analysts, data engineers, product managers, technology contributors and leaders, or individuals interested in learning more about this spaceCode of Conduct Please review and agree to adhere to our Code of Conduct to participate in our community.