Algorithm team lead

Summary: Lead the data science and data engineering teams to build and scale data infrastructure for real-time pricing and machine learning products. Responsibilities include managing sprint planning, mentoring staff, and contributing hands-on to architecture and development of mission-critical systems.

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 Algorithm Team Lead - Optimization 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.

Responsibilities:

• Manage a team of data scientists and data engineers responsible for building robust, scalable, and high-performance data pipelines and infrastructure.
• Design, build, and maintain distributed data processing workflows (batch & 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 for your team.
• Contribute hands-on to key development tasks and architecture decisions.
• Recruit, mentor, and grow the data science engineering team.
• Proven experience as a Data Scientist.
• Proven experience designing and maintaining large-scale data platforms (hundreds of TBs)
• 3+ years of proven experience leading and managing a team of data engineers (people management is required)
• 4+ years of experience in data science, including a strong Python programming background, advanced SQL skills, and data modeling experience
• Expertise with data orchestration tools (Airflow, Prefect, or Dagster)
• Cloud platform experience — GCP preferred (AWS/Azure acceptable)
• Familiarity with Docker
• Strong communication, mentorship, and collaboration skills
• BSc/MSc in Mathematics/ physics/ statistics/ Computer Science.
• Fluent English (spoken and written)

Nice to have:

• Experience with Google Cloud Platform tools (BigQuery, Dataflow, Pub/Sub)
• Background working with ML pipelines or data science teams
• Understanding of data contracts, lineage, and versioning tools
• Knowledge of data structures and algorithms
• Experience optimizing pipelines and databases for performance and cost
• CI/CD pipelines (GitHub/GitLab)
• Hands-on experience with distributed data frameworks (Apache Beam, Spark, or similar)
• Exposure to airline, pricing, or GDS systems