Team Lead Data Scientist

Fetcherr experts in deep learning, 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.

As our company continues its rapid growth, we're looking for a highly experienced and impactful Team Lead Data Scientist to join our team. This role is ideal for someone who not only possesses deep technical expertise but also demonstrates the potential to lead and shape our data science initiatives as we scale. You will be instrumental in translating complex data into strategic business insights, driving data-driven decision-making, and mentoring team members.

You'll be a great fit if

• You have 6+ years of hands-on data analytics and data science experience, including at least 2 years in a technical leadership role, with a proven track record of leading cross-functional data initiatives that drive measurable business outcomes.
• You are highly proficient in Python (e.g., pandas, numpy, scikit-learn, statsmodels) and the broader data science and machine learning ecosystem. You can review, guide, and challenge modeling decisions, code quality, and solution architecture across the team.
• You have excellent review discipline (e.g., testing, versioning, documentation). You ensure technical rigor, model reliability, and reproducibility in all deliverables.
• You're an exceptional communicator and storyteller, able to translate complex modeling results into executive-level insights. You help the team craft narratives that influence C-level decision-making and align with business strategy.
• You excel at mentoring and growing data scientists, providing clear direction, feedback, and career development support. You foster an environment of psychological safety, high accountability, and continuous learning.
• You take ownership of the team's roadmap and backlog, collaborating with product, project, and business leaders to ensure analytical initiatives are prioritized, scoped, and delivered with impact.
• You have a track record of identifying high-leverage opportunities, scoping ambiguous problems into structured workstreams, and delivering analytical solutions that drive strategic decisions and product evolution.
• You're comfortable balancing hands-on work (e.g., designing solution approaches, prototyping critical models) with team coordination, stakeholder alignment, and delivery oversight.
• You demonstrate business intuition and a clear understanding of how ML/DS systems interact with user behavior, product decisions, and key business metrics. You help the team focus on what matters.
• You are fluent in SQL, with experience in cloud-based data environments (e.g., BigQuery, Dataform, GCP/AWS). You enforce good data hygiene and ensure data reproducibility and traceability.
• You are deeply collaborative, often acting as the bridge between data science, product, engineering, and leadership, ensuring alignment between technical execution and strategic business objectives.
• You have strong English communication skills, both written and verbal, and feel comfortable working in an environment where English is the primary language for collaboration, documentation, and presentations.

Great if you also have…

• Prior experience leading analytics or data science teams in the travel industry (airlines, hospitality, transportation), with awareness of its specific modeling and operational complexities.
• Hands-on expertise in causal inference, experimental design, and econometrics (e.g., difference-in-differences, uplift modeling, advanced time series forecasting).
• Experience in mathematical optimization and its application to pricing, scheduling, logistics, or resource allocation problems.
• Proven ability to lead the deployment of production-grade ML models, with a clear understanding of MLOps, monitoring, version control, and failure modes.