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Fetcherr builds responsible AI that transforms market complexity into measurable profit growth through its proprietary Market Model—an AI-powered system delivering 96% forecast accuracy and real-time decision intelligence. The company's glass-box architecture uses market data with full transparency, deployed first in global aviation across partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul, delivering an average 7% profit uplift.
You will lead the Algorithm Team - Optimization, managing a team of data scientists and data engineers responsible for building robust, scalable, high-performance data pipelines and infrastructure. The Price Optimization team sits at the core of decision-making, working with large-scale customer data and market predictions to drive revenue management decisions. You will design and maintain the decision-making engine: data pipelines that ingest customer data and run it through an optimization engine that simulates the market, weighing competition, pricing constraints, inventory availability, predictive models, and client business policies to generate price recommendations.
Key responsibilities include:
- Manage a team of data scientists and data engineers
- Design, build, and maintain distributed data processing workflows (batch and streaming)
- Drive best practices for data quality, validation, testing, and observability
- Own and evolve Fetcherr's data architecture in alignment with business and product goals
- Manage sprint planning, task breakdown, code reviews, and performance feedback
- Contribute hands-on to key development tasks and architecture decisions
- Recruit, mentor, and grow the data science engineering team
This role combines hands-on technical leadership with team management, requiring attention to detail and ownership of mission-critical systems that power real-time pricing and large-scale data pipelines.
Requirements: The posting does not explicitly state years of experience, required skills, education, or certifications. However, the role implies expertise in distributed data systems, data pipeline architecture, machine learning infrastructure, team leadership, and experience managing data science/engineering teams.
