About the role

Lead Data Scientist on Operations data science team supporting Finance with machine learning and AI solutions. Impact financial decision-making and resource allocation in a fast-growing firm.

Responsibilities

Lead the design, development, and deployment of advanced machine learning, forecasting, optimization, and generative AI solutions
Partner closely with Finance leadership and other key business stakeholders
Build machine learning systems from the ground up and design scalable data science infrastructure
Strategically determine the most appropriate modeling approach for each problem
Deliver cutting-edge forecasting, anomaly detection, and optimization expertise
Transform model outputs into actionable, executive-ready business recommendations
Champion and execute long-term strategic projects
Collaborate closely with engineering and product teams to seamlessly deploy models into production
Mentor data scientists on the team

8+ years as a data scientist developing predictive business models
At least 5 years of experience in time series forecasting, anomaly detection, and/or optimization
MS/Ph.D in Data Science, Statistics, Applied Mathematics, Engineering, or similar quantitative discipline
Strong proficiency in SQL and Python
Deep expertise in the data science ecosystem (e.g., PyTorch, Transformers, scikit-learn, XGBoost/LightGBM, scipy, statsmodels)
Deep experience building and deploying forecasting models and resource-constrained optimization models in production environments
Demonstrated experience applying Generative AI to real business problems
Experience building Agentic Workflows and implementing RAG architectures
Strong foundation in statistics, including hypothesis testing, experimental design, and causal inference
Hands-on experience with cloud platforms, with a strong preference for Microsoft Azure
Ability to combine model results with clear business understanding to create actionable recommendations