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Salary: USD 298,700 - 479,400 / annual
ServiceTitan is seeking a Senior Director of Data Science and Analytics to lead data science and applied AI across operational and finance domains. This is a player-coach leadership role where you'll set technical direction while remaining hands-on in delivering high-impact projects.
Key Responsibilities:
- Own the data science and applied AI roadmap for G&A, including agentic systems, forecasting, lead scoring, and decision-support tools that improve throughput, reliability, and unit economics.
- Lead, hire, and develop a data science and analytics team executing on operational and product-facing work; establish standards for technical rigor, evaluation, and production quality.
- Partner with operations and product leadership to identify high-leverage problems and translate them into operational decisions and shipped AI capabilities.
- Oversee the finance analytics team supporting accounting, FP&A, and pricing functions.
- Collaborate with data engineering, machine learning engineering, and platform teams on infrastructure, data quality, orchestration, tooling, and guardrails.
- 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 and by inventing agentic systems that accelerate team velocity.
The role offers significant latitude to work on and invent new high-ROI projects with creative freedom.
Requirements:
- 8+ years in data science or applied AI/ML, with 5+ years leading and growing teams.
- Strong background managing analytics teams.
- Demonstrated track record influencing internal operational decisions based on ML systems (e.g., forecasts, lead scores).
- Strong foundation in statistics and ML, plus depth in modern AI: LLMs, agentic systems, orchestration (tool use, MCP), retrieval, and evaluation practices.
- Proven expertise in analytics and driving decisions through deep analysis.
- 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 fast-moving, data-rich environments where decisions carry real operational and cost consequences.
Nice to Have:
- Background in B2B SaaS, marketplaces, logistics, or field operations.
