Data Scientist Engineer

Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. At the core of the company is the Market Model - a proprietary AI-powered model delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture, it uses market data - not personal data - with full transparency into logic and outcomes. First deployed in global aviation, the technology is industry-agnostic and scales across volatile markets. Fetcherr delivers a consistent average profit uplift of 7%, with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul. The Price Optimization (PO) team is where insights become decisions. We work with large-scale customer data and market predictions — like demand forecasts — to drive revenue management decisions that actually move the needle. At our core, we build and maintain the decision-making engine. That means designing the data pipelines that bring customer data in, and running it through an optimization engine that simulates the market — weighing competition, pricing constraints, inventory availability, predictive models, and each client's unique business policies — to generate the best possible price recommendations. As the final step before recommendations reach the client, quality, reliability, and attention to detail aren't just nice to have. They're everything. We are looking for a Data Science Engineer to design and implement the decision-making logic of our Price Optimizer. You will focus on translating complex business rules into mathematical models and production-ready Python code. Your primary mission is to build the "brain" of our system. You will work at the intersection of Data Science and Product, ensuring our simulation and optimization engines accurately reflect real-world pricing strategies and market dynamics. While you will write production code, you will rely on our Data Engineers for ETL pipelines orchestration and distributed compute scaling.