Data Science Team Leader

Data Science Team Leader

Full-Time 80000 - 100000 £ / year (est.) Home office (partial)
Just Eat

At a Glance

  • Tasks: Lead a team of Data Scientists to enhance delivery time predictions and drive strategic solutions.
  • Company: Join Just Eat, a global leader in online food delivery with a vibrant culture.
  • Benefits: Enjoy competitive pay, flexible work options, and opportunities for professional growth.
  • Other info: Be part of an inclusive team that values diversity and fosters personal growth.
  • Why this job: Make a real impact on logistics while working with cutting-edge data science technologies.
  • Qualifications: Master's or PhD in Data Science, plus leadership experience and machine learning expertise.

The predicted salary is between 80000 - 100000 £ per year.

hackajob is collaborating with Just Eat to connect them with exceptional professionals for this role. Hungry for a challenge? That’s good, because at Just Eat (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.

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.

Location: Berlin, London or Amsterdam office with 3 days in the office and 2 days working from home

Reporting to: Data Science Manager

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.

What You'll Bring to the Table

  • 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.

At JET, this is how we play. Our teams forge connections internally and work with some of the best-known brands on the planet, giving us truly international impact in a dynamic environment. Being the best at what we do isn’t just about delivering on our strategy. It's a competition for something incredibly valuable – our customers' choice. Every time a customer decides where to order, they're picking a side. At the heart of the JET Customer League are our values and behaviours. They guide every interaction, every decision, every innovation. These are the actions we need to perform consistently and brilliantly, to surpass the competition and earn our customers’ loyalty, again and again. Fun, fast-paced and supportive, the JET culture is about movement, growth, helping one another to succeed and celebrating wins. By truly living our values and embodying our behaviours, we’re building a customer-first culture which enables us to stay one step ahead of the competition.

Inclusion, Diversity & Belonging

No matter who you are, what you look like, who you love, or where you are from, you can find your place at Just Eat. We’re committed to creating an inclusive culture, encouraging diversity of people and thinking, in which all employees feel they truly belong and can bring their most colourful selves to work every day.

Are you ready to join the team? Apply now!

Just Eat

Contact Details:

Just Eat Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Data Science Team Leader

Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Just Eat!

Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Science Team Leader at Just Eat.

Leverage Professional Networks

Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Just Eat.

Apply Directly through Our Website

When you find a suitable opening like Data Science Team Leader at Just Eat, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace Data Science Team Leader

Leadership
Team Management
Data Science
Machine Learning
Predictive Modelling
Root Cause Analysis
Stakeholder Communication

Some tips for your application 🫡

Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!

Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!

Craft a Tailored Cover Letter:For a full-time role at Just Eat, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.

Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Just Eat. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!

How to prepare for a job interview at Just Eat

Brush Up on Your Statistics

For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!

Showcase Your Projects

Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!

Get Comfortable with Python and R

Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Just Eat!

Prepare for Case Studies

Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.