Director, Data Science

ServiceTitan ServiceTitan · Enterprise · United States · Remote

Director of Data Science for Operations to lead data science and applied AI across operational core and post-sales product experience. This role involves setting technical direction, building and developing a team, and owning outcomes for internal operational efficiency and customer-facing AI products like support agents and onboarding. Responsibilities include owning the AI roadmap for operations (agentic systems, forecasting, lead scoring), owning AI for post-sales product experiences (support agent, onboarding intelligence), leading and developing a data science team, partnering with leadership to identify high-leverage problems, driving the full lifecycle from problem definition to monitoring, establishing metrics and evaluation frameworks, and collaborating with various engineering and product teams. The role requires strong foundations in statistics, ML, LLMs, agentic systems, orchestration, retrieval, and evaluation practices, with experience building and owning production AI systems.

What you'd actually do

  1. 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.
  2. 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.
  3. Lead, hire, and develop a data science team executing on operational and product-facing work; set standards for technical rigor, evaluation, and production quality.
  4. Partner with operations, finance, and product leadership to identify high-leverage problems, frame them, and translate them into operational decisions and shipped AI capabilities.
  5. 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.

Skills

Required

  • 8+ years in data science or applied AI/ML
  • 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.

Nice to have

  • Background in B2B SaaS, marketplaces, logistics, or field operations.
  • Familiarity with customer success, onboarding, or support operations as a domain.

What the JD emphasized

  • partner with operations, finance, product, and engineering leadership
  • player-coach leadership role
  • set technical direction
  • build and develop a team
  • own outcomes
  • significant latitude to work on and invent new high ROI projects
  • agentic systems
  • support agents
  • onboarding intelligence
  • customer success automation
  • production deployment
  • evaluation
  • quality monitoring
  • technical rigor
  • production quality
  • identify high-leverage problems
  • frame them
  • translate them into operational decisions
  • shipped AI capabilities
  • full lifecycle from problem definition through deployment and monitoring
  • systems hold up in production
  • autonomous workflow
  • live customer interaction
  • metrics and evaluation frameworks
  • rigorous evaluation of non-deterministic AI systems
  • data engineering
  • machine learning engineering
  • platform
  • product teams
  • infrastructure
  • data quality
  • orchestration
  • tooling
  • guardrails
  • executive stakeholders
  • AI/agent-first way of working
  • hands-on technical leader
  • invent agentic systems
  • 8+ years in data science or applied AI/ML
  • 5+ years leading and growing teams
  • Demonstrated track record deploying systems that delivered measurable impact
  • internal operational decisions
  • customer-facing AI features
  • LLMs
  • agentic systems
  • orchestration
  • tool use
  • MCP
  • retrieval
  • evaluation practices
  • production AI systems
  • agents
  • conversational systems
  • autonomous workflows
  • eval
  • monitoring
  • safety practices
  • SQL
  • Python
  • modern data and AI stacks
  • cloud warehouses
  • dbt
  • LLM/agent deployment pipelines
  • partner with non-technical executives
  • translate ambiguous business problems into tractable work
  • fast-moving, data-rich environment
  • decisions carry real operational, cost, and customer-experience consequences

Other signals

  • leading data science and applied AI roadmap
  • building and developing a team
  • owning outcomes across both internal operations and in-product intelligence
  • driving the full lifecycle from problem definition through deployment and monitoring
  • establishing metrics and evaluation frameworks
  • collaborating with data engineering, machine learning engineering, platform, and product teams
  • communicating findings and recommendations to executive stakeholders
  • championing an AI/agent-first way of working