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Fetcherr is an AI company building responsible, transparent AI systems that transform market complexity into measurable profit growth. The core product is the Market Model—a proprietary AI-powered system delivering 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture using market data with full transparency, it's deployed across global aviation with corporate partners including Delta, Virgin Atlantic, WestJet, and others, delivering consistent 7% average profit uplift.
You will lead the Data Science and Data Engineering team, overseeing skilled data engineers and data scientists. This is a hands-on technical leadership role combining team management with direct contribution to mission-critical systems.
The Price Optimization (PO) team—where you'll operate—transforms insights into decisions. The team works with large-scale customer data and market predictions (demand forecasts) to drive revenue management decisions. You'll design and maintain the decision-making engine: data pipelines that ingest customer data, run it through an optimization engine that simulates the market (weighing competition, pricing constraints, inventory, predictive models, and client business policies), and generate price recommendations. Quality, reliability, and attention to detail are paramount.
Key responsibilities include: managing a team of data scientists and engineers building robust, scalable, high-performance data pipelines and infrastructure; designing, building, and maintaining distributed data processing workflows (batch and streaming); driving best practices for data quality, validation, testing, and observability; owning and evolving Fetcherr's data architecture aligned with business and product goals; managing sprint planning, task breakdown, code reviews, and performance feedback; contributing hands-on to key development and architecture decisions; and recruiting, mentoring, and growing the team.
This role requires technical depth in data engineering and ML systems, leadership experience managing technical teams, and the ability to balance strategic architecture decisions with hands-on execution.
