Lead Data Scientist

Key Responsibilities

• Lead end-to-end data science projects by understanding business needs, scoping solutions, and delivering measurable outcomes.
• Act as a technical leader who advises, mentors, and delegates to junior data scientists and analysts.
• Develop and own financial forecasting models, including revenue projections, cost modeling, budget variance analysis, and scenario planning, working closely with Finance and FP&A teams.
• Apply the latest machine learning and deep learning techniques, including gradient boosting (XGBoost, LightGBM), transformer-based models, and time-series frameworks (Prophet, TFT) to build production-ready predictive solutions.
• Leverage generative AI and large language model (LLM) capabilities, including agentic AI architectures, Retrieval-Augmented Generation (RAG), and prompt engineering, to build intelligent data products and automate analytical workflows.
• Design and implement MLOps practices including model versioning, monitoring, automated retraining pipelines, and CI/CD for ML models to ensure scalability and reliability in production.
• Enhance data collection and feature engineering procedures to improve analytical systems; perform data discovery across internal and external sources to assess data value and relationships.

Requirements

• Experience with financial forecasting and a command of modern AI/ML frameworks are essential to this role.
• Experience 5+ years of hands-on industry experience designing and implementing machine learning models and data science solutions, with at least 3 years in a lead or senior individual contributor role.
• Demonstrated experience in financial forecasting and FP&A analytics including time-series forecasting, budget modeling, revenue/cost prediction, and scenario analysis.
• Prominent track record of deploying machine learning models to production at scale using cloud platforms (AWS, Azure, or GCP) and MLOps tooling.
• Experience working cross-functionally with Finance, Technology, and Operations stakeholders to translate business requirements into analytical solutions.

Qualifications

• Masters degree (or higher) in Applied Mathematics, Statistics, Data Science, Computer Science, Economics, Finance, or Engineering.

Skills

• Financial forecasting
• Machine learning and deep learning techniques
• Generative AI and large language model (LLM) capabilities
• MLOps practices
• Data collection and feature engineering

Benefits

N/A

Pay

N/A

Schedule

N/A