Sr. Data Scientist, Gen AI Automation

Job Description

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . Are you passionate about applying GenAI to improve efficiency and accelerate business execution—grounded in strong data foundations? Join Pinterest’s IT Enterprise Systems team to build analytics engineering and GenAI capabilities that power our go-to-market and corporate operations. You’ll create trusted data models and pipelines across core enterprise platforms and layer in LLM-enabled workflows with clear evaluation, observability, and guardrails. What you'll do: Build production-grade GenAI-enabled analytics solutions that integrate enterprise data sources (e.g., Salesforce, Gong, Oracle, IBM Planning Analytics, and other SaaS platforms). Establish end-to-end LLM pipelines (retrieval/orchestration) with evaluation frameworks, observability, and validation guardrails to ensure reliability and safety. Lead technical scoping for GenAI use cases, assessing feasibility, accuracy expectations, risk, and ROI—then translating into clear technical plans. Design and own analytics-ready data models, including dimensional modeling (star schemas, fact/dimension tables, SCD Type 2) that support reporting, forecasting, and downstream applications. Develop and maintain robust data pipelines and orchestration (Airflow and/or dbt or similar), including data quality checks, SLAs, monitoring, and failure recovery. Write and optimize complex SQL for transformation and analysis across enterprise datasets (joins, window functions, CTEs, performance tuning). Partner closely with business stakeholders, turning ambiguous questio

Required Skills