Sr. Data Scientist, Gen AI Automation

Pinterest Pinterest · Consumer · San Francisco, CA · IT

This role focuses on building production-grade GenAI-enabled analytics solutions by integrating enterprise data sources and establishing LLM pipelines with evaluation, observability, and guardrails. It involves designing data models, developing data pipelines, writing SQL, and defining best practices for GenAI in analytics engineering.

What you'd actually do

  1. Build production-grade GenAI-enabled analytics solutions that integrate enterprise data sources (e.g., Salesforce, Gong, Oracle, IBM Planning Analytics, and other SaaS platforms).
  2. Establish end-to-end LLM pipelines (retrieval/orchestration) with evaluation frameworks, observability, and validation guardrails to ensure reliability and safety.
  3. Lead technical scoping for GenAI use cases, assessing feasibility, accuracy expectations, risk, and ROI—then translating into clear technical plans.
  4. 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.
  5. Develop and maintain robust data pipelines and orchestration (Airflow and/or dbt or similar), including data quality checks, SLAs, monitoring, and failure recovery.

Skills

Required

  • 8+ years in analytics engineering, data engineering, data science, or related roles
  • Strong understanding of LLM fundamentals and practical tradeoffs
  • Expert SQL skills (complex joins, window functions, CTEs, query optimization)
  • Hands-on experience with dimensional modeling (star schemas, SCD Type 2)
  • Hands-on experience with workflow orchestration tools such as Airflow, dbt
  • Demonstrated ability to validate AI-assisted output
  • High integrity and ownership to handle sensitive enterprise data appropriately
  • Strong stakeholder communication
  • Experience with GenAI coding tools (e.g., Cursor, Claude Code)

Nice to have

  • Bachelor’s degree in Computer Science, Statistics, Data Science, Mathematics, or a related field (or equivalent experience)

What the JD emphasized

  • GenAI-enabled analytics solutions
  • LLM pipelines
  • evaluation frameworks
  • observability
  • validation guardrails
  • GenAI use cases
  • GenAI in analytics engineering

Other signals

  • integrating enterprise data sources
  • LLM-enabled workflows
  • analytics engineering
  • GenAI capabilities