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. While Fetcherr initially focused on the airline industry, the company is now expanding into additional verticals, bringing its dynamic AI-driven optimization capabilities to new industries such as travel, mobility, retail, e-commerce, and beyond.
About the Role
This is not a pure research role but one that is actively involved in building our product. You will be joining a team of Data Software Engineers and you will be directly contributing to code. You will own well- scoped modeling problems end to end, from exploring market data and designing features to validating models and getting them running in production, so that our pricing, inventory, and network optimization engines make better decisions in real time. This is a high-impact individual contributor role at the intersection of applied AI and production software, working directly with other data scientists, ML and data engineers, and product teams.
What you will do:
- Develop, validate, and iterate on demand forecasting, pricing, and optimization models using large-scale, high-velocity market data.
- Explore and analyze data from diverse internal and external sources to uncover signals, define features, and identify opportunities to improve model accuracy and business impact.
- Design and run offline experiments, backtests, and online evaluations to measure model performance and profit uplift.
- Write production-grade Python code and work with ML and data engineers to productionize models, ensuring they are reliable, observable, and cost-efficient.
- Monitor deployed models for drift, accuracy, and data quality issues, and troubleshoot and fix problems quickly.
- Keep our glass-box principles front and center: build models whose logic and outcomes can be explained to commercial teams and customers.
- Partner with product and commercial stakeholders to translate business problems into clear modeling approaches and communicate results.
- Contribute to the technical direction of the modeling stack as Fetcherr scales across new customers, verticals, and product lines.