Director of Data Science, ServiceTitan

• 8+ years in data science or applied AI/ML, with 5+ years leading and growing teams
• Demonstrated track record deploying systems that delivered measurable impact—across both internal operational decisions and customer-facing AI features
• Strong foundation in statistics and ML, plus depth in modern AI: LLMs, agentic systems, orchestration (e.g., tool use, MCP), retrieval, and the evaluation practices these require
• Experience building or owning production AI systems—agents, conversational systems, or autonomous workflows—and the eval, monitoring, and safety practices they demand
• Fluency in SQL and Python; familiarity with modern data and AI stacks (cloud warehouses, dbt, LLM/agent deployment pipelines)
• Proven ability to partner with non-technical executives and translate ambiguous business problems into tractable work
• Comfort operating in a fast-moving, data-rich environment where decisions carry real operational, cost, and customer-experience consequences

Desirable

• We're seeking a Director of Data Science for Operations to lead data science and applied AI across two connected domains: our operational core and the post-sales product experience
• You'll partner with operations, finance, product, and engineering leadership to turn complex data into intelligent systems, decisions, and measurable impact—spanning internal operational efficiency and customer-facing AI products like support agents and onboarding
• This is a player-coach leadership role: you'll set technical direction, build and develop a team, and own outcomes across both internal operations and in-product intelligence
• This role has significant latitude to work on and invent new high ROI projects- the only limit is your creativity
• Own the data science and applied AI roadmap for operations—agentic systems, forecasting, lead scoring, voice of the customer aggregation, and decision-support that improve throughput, reliability, and unit economics. Build agents and AI workflows that don't just predict but act
• Own the data science and AI behind post-sales product experiences, including the support agent, onboarding intelligence, and customer success automation—from design through production deployment, evaluation, and quality monitoring
• Lead, hire, and develop a data science team executing on operational and product-facing work; set standards for technical rigor, evaluation, and production quality
• Partner with operations, finance, and product leadership to identify high-leverage problems, frame them, and translate them into operational decisions and shipped AI capabilities
• Drive the full lifecycle from problem definition through deployment and monitoring, ensuring systems hold up in production—whether powering an internal forecast, an autonomous workflow, or a live customer interaction
• Establish metrics and evaluation frameworks connecting this work to operational, financial, and customer outcomes (e.g., deflection, resolution quality, time-to-value, retention), including rigorous evaluation of non-deterministic AI systems
• Collaborate with data engineering, machine learning engineering, platform, and product teams on the infrastructure, data quality, orchestration, tooling, and guardrails these systems depend on
• Communicate findings and recommendations to executive stakeholders, balancing technical depth with business clarity
• Champion an AI/agent-first way of working within the team—both as a hands-on technical leader (e.g., Claude Code) and by inventing agentic systems that make the team faster