As the Lead Data Scientist on our Operations data science team supporting the Finance organization, you will use machine learning, generative AI, and data-driven insights to drive financial planning, optimize spend, and inform strategic decision-making across ServiceTitan. Your work will directly impact some of ServiceTitan's most critical financial decisions and help shape how the company forecasts, plans, and allocates resources at scale. This is a rare opportunity to blend machine learning and GenAI with strategic financial thinking at a fast-growing firm in a way that will be visible to top executives within ServiceTitan. What you'll do: - Lead the design, development, and deployment of advanced machine learning, forecasting, optimization, and generative AI solutions that power financial planning, analysis, and decision-making - Partner closely with Finance leadership and other key business stakeholders to translate financial questions into rigorous data science problems - Build machine learning systems from the ground up and design scalable data science infrastructure that Finance teams can rely on - Strategically determine the most appropriate modeling approach (traditional ML, forecasting, optimization, or Generative AI) for each problem, and design experiments to evaluate competing approaches - Deliver cutting-edge forecasting, anomaly detection, and optimization expertise, driving best practices across financial modeling at ServiceTitan - Transform model outputs into actionable, executive-ready business recommendations - Champion and execute long-term strategic projects that improve how ServiceTitan plans and operates financially - Collaborate closely with engineering and product teams to seamlessly deploy models into production, ensuring scalability and performance - Mentor data scientists on the team and elevate the technical bar across forecasting, optimization, and GenAI work - Invent bold and creative solutions to current and future business problems What you'll bring: - Experience: 8+ years as a data scientist developing predictive business models, with at least 5 years of experience in time series forecasting, anomaly detection, and/or optimization. Proven track record of delivering impactful, production-grade solutions. - Education: MS/Ph.D in Data Science, Statistics, Applied Mathematics, Engineering, or similar quantitative discipline. - Core Technical Stack: Strong proficiency in SQL and Python, with deep expertise in the data science ecosystem (e.g., PyTorch, Transformers, scikit-learn, XGBoost/LightGBM, scipy, statsmodels). - Forecasting & Optimization: Deep experience building and deploying forecasting models and resource-constrained optimization models in production environments. - Generative AI & Agents: - Demonstrated experience applying Generative AI to real business problems, including practical experience developing and testing GenAI solutions - Experience building Agentic Workflows and implementing RAG architectures - Statistical Rigor: Strong foundation in statistics, including hypothesis testing, experimental design, and causal inference to ensure the reliability of analytical insights and model impact evaluation. - Cloud Infrastructure: Hands-on experience with cloud platforms, with a strong preference for Microsoft Azure (e.g., Azure AI Studio, Foundry). - Business Acumen: Ability to combine model results with clear business understanding to create actionable recommendations, particularly in financial or operational contexts. - Soft Skills: Excellent communication skills, including translating complex technical concepts into actionable insights for finance and executive stakeholders. Proactive and collaborative mindset. - Passion for discovery, problem solving, and practical business outcomes in partnership with finance and engineering leadership. Preferred experience: - Experience supporting Finance, FP&A, or related functions with data science solutions - Experience building and deploying MCPs