• Lead the research process: Partner with our Chief AI Officer to set the research agenda, decide which questions are worth answering, and run the process from hypothesis to evidence to recommendation.
• Partner with the business: Work with leaders, customers, and subject matter experts to find where data science moves the business, such as driver risk, fraud and anomalies, retention, pricing, and compliance prediction. Size each opportunity, frame it, and make the case in plain language.
• Build a forward-looking view of the business: Work with ML engineers and data engineers to develop forecasts, leading indicators, and models that help leadership see where the business is heading and act earlier.
• Apply rigorous methods: Learn how Foley's data is created before trusting it. Lead EDA, build and test hypothesis trees, engineer domain-driven features, and build production-ready models. Validate with holdouts, leakage checks, calibration, error analysis, and bias and fairness review.
• Treat data as a strategic asset: Size the value of Foley's data, identify gaps worth closing, evaluate external data to buy or license, and help shape and price data products.
• Communicate and take insights to product: Explain to executives, customers, and engineers what a model does, what it does not do, and how confident we are in it. Turn findings into product proposals and define success metrics before launch.