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Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. The company's core technology is the Market Model—a proprietary AI-powered system delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture using market data with full transparency, it has been deployed in global aviation and scales across volatile markets, delivering an average profit uplift of 7% for corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul.
The Price Optimization (PO) team translates insights into decisions by working with large-scale customer data and market predictions to drive revenue management decisions. The team builds and maintains the decision-making engine—designing data pipelines that ingest customer data 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 optimal price recommendations.
As a Data Scientist Engineer, you will design and implement the decision-making logic of the Price Optimizer, focusing on translating complex business rules into mathematical models and production-ready Python code. Your primary mission is to build the "brain" of the system, working at the intersection of Data Science and Product to ensure simulation and optimization engines accurately reflect real-world pricing strategies and market dynamics.
Key responsibilities include: implementing business logic by translating intricate pricing rules and commercial strategies into robust Python code and mathematical constraints; refining optimization models by developing and tuning simulation and revenue management algorithms; writing clean, tested, and maintainable code for core logic repositories; analyzing and improving model behavior using data to identify edge cases where business rules clash with algorithmic outputs; and collaborating with Solution Architects to define logic requirements and with Data Engineers to integrate models into Dagster pipelines.
While you will write production code, you will rely on Data Engineers for ETL pipeline orchestration and distributed compute scaling.
