Data Science Team Leader

Hungry for a challenge?

That's good, because at Just Eat Takeaway.com (JET) we have abundant opportunity, or, as we say, everything is on the table. We are a leading global online food delivery marketplace. Our tech ecosystem connects millions of active customers with hundreds of thousands of connected partners in countries across the globe.

Our mission? To empower every food moment around the world, whether it's through customer service, coding or couriers.

• About this role
• As the Senior Team Lead for Data Science - Logistics Estimations, you will be the strategic leader responsible for maximizing the impact of our predictive engine on the global delivery network. Your primary focus will be providing vision, strategic oversight, and leadership to a fully experienced team of Data Scientists specializing in Estimated Time of Arrival (ETA) prediction and other critical logistics estimations.
• This is a strategic leadership role where your focus is on defining data science solutions, collaborating on the roadmap, driving business outcomes, and expertly managing and developing your team. Experience in model development and deployment is essential for providing effective technical guidance and strategy to the team.
• Your core mission is to elevate the accuracy of pre-purchase and post-purchase estimated delivery time models by translating business performance challenges into data science solutions.
• The Key Ingredients of the role
• Strategic Leadership & Outcome Ownership (Approx. 75%)
• Team Leadership & People Management: Lead, manage, and scale a high-performing team of Data Scientists through mentorship, coaching, and performance management. Foster a collaborative and results-driven team culture.
• Drive Strategic Solutions: Own the team's contribution to defining logistics estimation solutions, ensuring direct alignment with critical company objectives and the broader business strategy. You will champion data science innovation ideas and partner closely with Product Management (PM) to ensure work is prioritized based on business opportunities. You will proactively manage dependencies across the cross-functional team. This includes continuous collaboration with Machine Learning (ML) and Backend Engineers, particularly for post-purchase Estimated Time of Arrival (ETA) implementation. Communicate the team's direction and impact to senior stakeholders.
• Drive Business Impact: Take ownership of key logistics KPIs, particularly overall ETA accuracy across the customer journey. Guide the team's focus on the highest-value initiatives and ensure data science moves in a data-driven direction. The DSTL partners closely with the Product Manager to advise and influence the strategic prioritization of work.
• Business Root Cause Analysis (RCA) & Solution Strategy: Drive deep analysis to uncover the root causes and underlying business drivers of accuracy gaps in ETA models. This includes diagnosing operational friction points (e.g., courier re-assignments, restaurant preparation delays) that impact model performance.
• Strategic Planning: Translate RCA findings into concrete, actionable plans and conceptual designs for incorporating new features or models to mitigate these business-driven inaccuracies.
• Stakeholder Communication: Act as the primary interface between the Data Science team and the wider audience (Product, Operations, Engineering). Expertly translate complex data insights and machine learning concepts into clear, actionable business insights for non-technical audiences.
• Technical Guidance & Conceptual Design (Approx. 25%)
• Conceptual Model Design: Focus on the conceptual design and validation strategy for new predictive features and models. Define what the model needs to achieve and how it should be validated.
• Technical Guidance & Review: Serve as the senior technical expert, guiding architectural decisions for prediction models and maintaining high standards through rigorous design discussions and model reviews.
• MLE Partnership: Partner closely with Machine Learning Engineers (MLEs), who own the deployment and scaling processes, to transition robust model designs and thoroughly documented algorithmic logic for production implementation.
• Insight Generation: Guide the team in deep-dive analysis of large-scale geospatial and real-time data to translate findings into actionable modeling recommendations.

• A Master's degree or PhD in Data Science, Computer Science, Statistics, or a related quantitative field.
• Proven, extensive experience in leadership and people management, with a demonstrated ability to mentor, guide, and develop Data Scientists.
• Prior hands-on experience developing, deploying, and maintaining machine learning models in a corporate environment. This experience is crucial for providing effective strategic and architectural guidance.
• Advanced conceptual proficiency in data science and machine learning methodologies, ideally with experience in logistics, geospatial analysis, and ETA prediction or routing problems. Experience with deep learning is considered a plus.
• Demonstrated experience in root-cause analysis of complex production model performance issues and the ability to translate those findings into effective business and technical solutions.
• Strong understanding of the model lifecycle and best practices, including testing, code reviews, and monitoring.
• Exceptional communication and stakeholder management skills, with the ability to influence technical peers and non-technical business leaders.