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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 is deployed across global aviation and scales to volatile markets. Fetcherr delivers 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.
You will join as a Data Scientist Engineer to design and implement the decision-making logic of the Price Optimizer. 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. While you will write production code, you will rely on Data Engineers for ETL pipeline orchestration and distributed compute scaling.
Key responsibilities include: translating intricate pricing rules and commercial strategies into robust Python code and mathematical constraints; developing and tuning simulation and revenue management algorithms that drive pricing recommendations; contributing high-quality, tested, and maintainable code to core logic repositories; using data to validate model behavior and identify edge cases where business rules clash with algorithmic outputs; and partnering with Solution Architects to define logic requirements and with Data Engineers to integrate models into Dagster pipelines.
Quality, reliability, and attention to detail are critical—these are not nice-to-have but essential to the role.
