Lead Data Scientist ( Financial Forecasting & Modeling), Enterprise Analytics, JLL Technologies

JLL Technologies Enterprise Data team is a central organization that oversees JLL’s data strategy. Enterprise Analytics is an internal consulting team that provides data-driven expertise to various business and support functions, with a growing focus on financial forecasting, AI-powered insights, and advanced predictive modeling. The Lead Data Scientist is a senior technical leader responsible for driving high-impact analytics initiatives, applying the latest machine learning and AI techniques to solve complex business problems.

The ideal candidate is passionate about using data and AI to drive business strategy and develop enterprise-grade data products. They will lead end-to-end data science projects, scoping, designing, and executing the vision, while collaborating with finance, operations, and technology stakeholders. Experience with financial forecasting and a command of modern AI/ML frameworks are essential to this role.

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.
  
  

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.
• Proven 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.
  
  

Education

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