Lead Data Scientist

ServiceTitan ServiceTitan · Enterprise · United States · Remote

Lead Data Scientist role focused on applying ML, GenAI, and data-driven insights to financial planning, optimization, and strategic decision-making. The role involves designing, developing, and deploying advanced solutions, partnering with Finance leadership, building ML systems and infrastructure, and mentoring junior data scientists. Experience with time series forecasting, anomaly detection, optimization, Generative AI, Agentic Workflows, and RAG is required.

What you'd actually do

  1. Lead the design, development, and deployment of advanced machine learning, forecasting, optimization, and generative AI solutions that power financial planning, analysis, and decision-making
  2. Partner closely with Finance leadership and other key business stakeholders to translate financial questions into rigorous data science problems
  3. Build machine learning systems from the ground up and design scalable data science infrastructure that Finance teams can rely on
  4. Strategically determine the most appropriate modeling approach (traditional ML, forecasting, optimization, or Generative AI) for each problem, and design experiments to evaluate competing approaches
  5. Deliver cutting-edge forecasting, anomaly detection, and optimization expertise, driving best practices across financial modeling at ServiceTitan

Skills

Required

  • SQL
  • Python
  • PyTorch
  • Transformers
  • scikit-learn
  • XGBoost/LightGBM
  • scipy
  • statsmodels
  • time series forecasting
  • anomaly detection
  • optimization
  • Generative AI
  • Agentic Workflows
  • RAG architectures
  • hypothesis testing
  • experimental design
  • causal inference
  • Microsoft Azure
  • Azure AI Studio
  • Foundry
  • communication skills

Nice to have

  • MS/Ph.D in Data Science, Statistics, Applied Mathematics, Engineering, or similar quantitative discipline
  • Experience supporting Finance, FP&A, or related functions with data science solutions
  • Experience building and deploying MCPs

What the JD emphasized

  • production-grade solutions
  • Generative AI
  • Agentic Workflows
  • RAG architectures

Other signals

  • Generative AI
  • Machine Learning
  • Agentic Workflows
  • RAG