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Data Scientist Team Lead
Data Scientist Team Lead
Fetcherr experts in deep learning, algo-trading, e-commerce, and digitization, Fetcherr disrupts traditional systems with its cutting-edge AI technology. At its core is the Large Market Model (LMM), an adaptable AI engine that forecasts demand and market trends with precision, empowering real-time decision-making. Specializing initially in the airline industry, Fetcherr aims to revolutionize industries with dynamic AI-driven solutions.
We're looking for a Data Scientist Team Lead to help us grow our data science team's capabilities. The Team Lead will be responsible for the design and development of new products that Fetcherr provides its clients in the airline industry, from ideation to production. The position also involves building a fast-moving team of data scientists and engineers to develop these products. The ideal candidate should have relevant experience as a data scientist and in leadership roles.
You’ll be a great fit if you have:
- B.SC or Master’s degree in Computer Science / Statistics / Math / Engineering
- Have 5+ years of experience developing data science projects with Python
- Fluent with data science stack libraries such as pandas, numpy, scipy, scikitlearn, pytorch
- Experience in package development, pipeline orchestration and ci/cd processes
- Strong analytical skills and a clear understanding of the business implications of product development
- A solid understanding of data structures and databases.
- Experience in writing production-grade code and follows best software engineering practices
- Eager to continuously learn while working
- Management experience
- Ability to work independently and lead complex projects from conception to completion.
Nice to have:
- You have a passion for solving complex problems and a strong intuition in mathematical optimization (experience with optimisation libraries such as PuLP/Gurubi is a plus)
- Domain knowledge in the travel industry (airlines, transportation, hospitality)
- Experience with graph and/or network analysis