Senior Data Scientist - Moveworks

ServiceNow ServiceNow · Enterprise · Mountain View, CA +1 · Engineering

Senior Data Scientist role focused on defining and governing critical product metrics, applying statistical and machine learning techniques to drive product initiatives, and analyzing engineering system performance. The role involves leading the design and development of scalable analytical systems, driving AI-native integrations, and providing technical mentorship. Key responsibilities include LLM evaluation methodology, building evaluation pipelines for agentic/LLM systems, and working with conversational analytics and AI-assisted data exploration tooling.

What you'd actually do

  1. Partner with executive, product and engineering teams to define and govern Moveworks’ critical product metrics, ensuring consistency, accuracy, and alignment across the company.
  2. Apply rigorous statistical and machine learning techniques to unearth the critical levers that drive our core metrics. Your insights will be the bedrock of our product initiatives, and you will be accountable for forecasting and measuring the impact of these initiatives on business outcomes.
  3. Analyze and measure core engineering system performance, isolating signal from noise by controlling for confounding variables (e.g., usage patterns, resource supply, and skill activation).
  4. Lead the design, development, and ownership of best practices, tools, and processes for building scalable and robust analytical systems. These systems will automate established analyses and empower cross-functional partners with self-serve insights.
  5. Drive the development of DS workflow and AI-native integrations (e.g., Superset MCP, Claude integrations) that extend self-serve data access and reduce time-to-insight for stakeholders.

Skills

Required

  • M.S. or Ph.D. in Data Science, Computer Science, Statistics, or a related quantitative field, plus 5+ years of progressive experience building and deploying production analytics or ML solutions for enterprise software products.
  • Demonstrated expertise in LLM evaluation methodology, including dataset curation, benchmarking, and regression detection for AI/LLM systems.
  • Proven experience architecting scalable data pipelines using distributed systems (e.g., PySpark/Spark SQL), orchestration frameworks (e.g., Airflow), and cloud platforms (e.g., AWS EMR, S3, Snowflake or equivalent).
  • Proficiency in Python and SQL; track record of translating complex analytical work into measurable business outcomes and operational KPIs.
  • Demonstrated ability to lead technical workstreams and drive cross-functional alignment.
  • Experience providing technical leadership and mentoring to data scientists or engineers.
  • Experience building evaluation pipelines or harnesses for agentic or LLM-powered systems.
  • Experience with conversational analytics, NLP, or AI-assisted data exploration tooling.
  • Prior work on self-serve analytics platforms or customer-facing data products at enterprise scale.

Nice to have

  • Experience using modern AI productivity tooling (e.g., Claude, Cursor, Codex) within an enterprise SDLC.

What the JD emphasized

  • LLM evaluation methodology
  • building evaluation pipelines or harnesses for agentic or LLM-powered systems

Other signals

  • LLM evaluation methodology
  • AI/LLM systems
  • agentic or LLM-powered systems
  • conversational analytics
  • AI-assisted data exploration