Director of Data Science

Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. At the core of the company is the Market Model - a proprietary AI-powered model delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture, it uses market data - not personal data - with full transparency into logic and outcomes. First deployed in global aviation, the technology is industry-agnostic and scales across volatile markets. Fetcherr delivers a consistent average profit uplift of 7%, with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul.

The Large Market Model group (LMM) is looking for a Director of Data Science to lead its production engineering, overseeing MLOps, ML engineering and Data engineering. The Director owns the end to end ML lifecycle of the market model, from the data pipelines that feed it, through model training and serving, and on to the monitoring that keeps it healthy once it is live in customer systems.

Crucially, the Director is responsible for setting the roadmap across these functions, alongside LMM management and senior executives, and for turning it into work each function can execute. We are looking for a manager who has led high paced, AI first groups, and who has carried deliveries for systems running live in front of customers.

RESPONSIBILITIES

• Decide priorities across the functions with their leads, and review the technical trade-offs yourself.
• Manage the leads who run these functions and hold each of them to what their team has committed to.
• Stay close to production. Own delivery dates, reliability, and what happens when something breaks.
• Own the infrastructure for model training, serving, and large scale data processing, including its cost.
• Decide which numbers (KPIs) the group is measured on, then track them and report them.
• Report the group's progress and risks to senior management, to product, and to the commercial teams.
• Work with the applied research team to move their results into production.
• Hire and keep senior engineers, and decide how the group is staffed.
• Set the engineering standards the group works to: CI/CD, automated testing, observability, and model monitoring.