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.
We are seeking an experienced Data Science Team Lead to lead our data science and data engineering efforts and oversee a team of skilled data engineers. This role combines hands-on technical leadership and team management, with responsibilities that include building and scaling data infrastructure to power real-time pricing, large-scale data pipelines, and machine learning products.
The Price Optimization (PO) team is where insights become decisions. We work with large-scale customer data and market predictions — like demand forecasts — to drive revenue management decisions that actually move the needle.
At our core, we build and maintain the decision-making engine. That means designing the data pipelines that bring customer data in, and 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 the best possible price recommendations. As the final step before recommendations reach the client, quality, reliability, and attention to detail aren't just nice to have. They're everything.
You will lead the team through architecture decisions, development, and deployment of mission-critical systems—while growing and mentoring a high-performing team.
What you'll do
Manage a team of data scientists and data engineers responsible for building robust, scalable, and high-performance data pipelines and infrastructure.