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Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. The company's core product 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 globally in aviation and scales across volatile markets, delivering an average profit uplift of 7%. Corporate partners include Delta, Virgin Atlantic, WestJet, Viva, and Azul.
The Large Market Model (LMM) group is seeking a Director of Data Science to lead production engineering, overseeing MLOps, ML engineering, and data engineering. The Director owns the end-to-end ML lifecycle of the market model—from data pipelines through model training and serving to production monitoring and health checks in customer systems.
Key responsibilities include:
- Setting priorities across MLOps, ML engineering, and data engineering functions alongside LMM management and senior executives, and translating roadmap into executable work.
- Managing the leads running these functions and holding teams accountable to commitments.
- Staying close to production: owning delivery dates, reliability, and incident response.
- Owning infrastructure for model training, serving, and large-scale data processing, including cost management.
- Defining and tracking KPIs for the group, reporting progress and risks to senior management, product, and commercial teams.
- Partnering with the applied research team to move research results into production.
- Hiring and retaining senior engineers; deciding group staffing and structure.
- Setting engineering standards for CI/CD, automated testing, observability, and model monitoring.
Requirements:
- Proven experience leading high-paced, AI-first engineering groups.
- Track record of delivering systems running live in front of customers.
- Strong technical foundation in MLOps, ML engineering, and data engineering.
- Ability to manage technical trade-offs and make priority decisions.
- Experience with production ML infrastructure, model serving, and monitoring.
