Lead Data Scientist

Role Overview

Lead Data Scientist on ServiceTitan’s Operations data science team, supporting the Finance organization. You will use machine learning, generative AI, and data-driven insights to improve financial planning, optimize spending, and inform strategic decisions.

Key Missions

• Design, develop, and deploy advanced machine learning solutions for financial planning, analysis, and decision-making.
• Collaborate with Finance leadership to translate financial questions into rigorous data science problems.
• Build end-to-end machine learning systems and design a scalable data science infrastructure for Finance teams.

Responsibilities

• Lead the design, development, and deployment of production-grade ML solutions.
• Use forecasting, optimization, and generative AI to support finance use cases.
• Work closely with Finance stakeholders and other key teams.
• Mentor data scientists on the team.

Requirements

• 8+ years as a data scientist building predictive business models.
• At least 5 years in time series forecasting, anomaly detection, and/or optimization in production.
• Demonstrated experience applying Generative AI to real business problems (developing and testing GenAI solutions).
• Strong forecasting and optimization experience, including resource-constrained optimization models.
• Strong statistical rigor: hypothesis testing, experimental design, and causal inference.
• Excellent communication skills; ability to translate complex technical concepts into actionable insights for finance and executives.
• Business acumen to combine model outputs with business context to produce recommendations.
• Strong proficiency in SQL and Python.
• Cloud infrastructure hands-on experience, strongly preferred Microsoft Azure (e.g., Azure AI Studio, Foundry).
• Education: MS/Ph.D in Data Science, Statistics, Applied Mathematics, Engineering, or a similar quantitative field.

Nice to Have

• Experience with Agentic workflows and RAG (Retrieval-Augmented Generation) architectures.
• Experience supporting Finance / FP&A with data science solutions.
• Experience building and deploying MCPs.
• Deep expertise in the data science ecosystem: PyTorch, Transformers, scikit-learn, XGBoost/LightGBM, SciPy, statsmodels.

Location / Work Model

• Full remote