Price optimization decision team leadניהול

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.

Requirements

Proven experience as a Data Scientist.

Proven experience designing and maintaining large-scale data platforms (hundreds of TBs)