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Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. The company's proprietary Market Model delivers 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, the technology is deployed across volatile markets including global aviation, with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul, delivering an average profit uplift of 7%.
We are seeking an experienced Data Science Team Lead to lead data science and data engineering efforts and oversee a team of skilled data engineers. This role combines hands-on technical leadership with team management. The Price Optimization team is where insights become decisions—working with large-scale customer data and market predictions to drive revenue management decisions. You will design and maintain the decision-making engine: data pipelines that bring customer data in, 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 price recommendations.
Key responsibilities include managing a team of data scientists and data engineers responsible for building robust, scalable, high-performance data pipelines and infrastructure. You will design, build, and maintain distributed data processing workflows (batch and streaming), drive best practices for data quality, validation, testing, and observability, and own Fetcherr's data architecture in alignment with business and product goals. You will manage sprint planning, task breakdown, code reviews, and performance feedback for your team, contribute hands-on to key development tasks and architecture decisions, and recruit, mentor, and grow the data science engineering team. Quality, reliability, and attention to detail are critical—these systems directly impact client revenue management decisions.
