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Fetcherr builds responsible AI that transforms market complexity into measurable profit growth through its proprietary Market Model—an AI-powered system delivering 96% forecast accuracy and real-time decision intelligence. The company's glass-box architecture uses market data with full transparency, deployed first in global aviation across carriers like Delta, Virgin Atlantic, WestJet, and Azul, delivering consistent 7% average profit uplift.
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 designs and maintains the decision-making engine: data pipelines that ingest customer data and feed it through an optimization engine that simulates the market, weighing competition, pricing constraints, inventory, predictive models, and client 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 building 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 validate outputs and identify edge cases where business rules clash with algorithmic results; and collaborating with Solution Architects on logic requirements and Data Engineers on model integration into Dagster pipelines.
While you will write production code, the Data Engineering team handles ETL pipeline orchestration and distributed compute scaling, allowing you to focus on the mathematical and business logic layer.
