Centre of Excellence Lead — Data Science & Machine Learning

At Too Good To Go, we have an ambitious goal: to inspire and empower everyone to fight food waste together.

40% of all food produced in the world is wasted. And that has a huge impact on the health of our planet, with 10% of greenhouse gas emissions coming from food waste.

We're more than an app: we are a certified B Corporation with a mission to empower everyone to take action against food waste, so alongside our marketplace app, we create educational tools, explore new business solutions — such as our Retail Technologies offering — and influence legislation to help reduce food waste.

We're growing fast: Our community of 120 million registered users and 180,000 active partners across 20 countries have together already prevented 500+ million meals from going to waste

Your mission

We are looking for a Centre of Excellence Lead to build and grow the data science and machine learning capability at the heart of our Data and Analytics organisation.

The Centre of Excellence (CoE) brings together our Data Scientists and ML Engineers into a single, centralised team that partners with Product and business domains to solve our most important problems. As its Lead, you will own the team's technical direction, raise the bar on delivery and standards, and develop the people in it while staying close enough to the work to coach hands-on.

As a marketplace, many of our most critical challenges revolve around supply-demand dynamics, user behaviour, and marketplace efficiency. Your team works on problems such as personalisation, pricing, optimisation, and forecasting, helping us move from reactive analysis to proactive, model-driven systems.

Your role

• Lead and grow the CoE team of Data Scientists and ML Engineers — owning performance management, coaching, hiring, and the development of each individual
• Set and raise technical standards across the team, from problem framing and methodology through to experimentation, productionisation, and impact measurement
• Stay hands-on as a player-coach — close enough to the technical work to guide design decisions, review approaches, and help unblock the team
• Strengthen the DS↔ML handoff, ensuring models move from prototype to production reliably and that the two disciplines collaborate closely from the start of each initiative
• Partner with Product and business leaders to identify high-impact opportunities where data science and ML can drive step-change improvements, and translate them into a focused team roadmap
• Represent the CoE to Product, Engineering, and leadership stakeholders, making the case for where and how advanced methods create the most value
• Partner with the People team to define the capability framework for the data science and ML domain
• Champion the responsible use of AI within the team's ways of working, advocating for best practices and integrating new tools where they add genuine value

Requirements

• Solid experience leading technical teams of a similar size (around 5 people), including performance management and developing engineers or scientists
• A strong technical foundation in data science or machine learning — you have done the technical work yourself and can credibly coach it
• Comfortable across both disciplines: you don't need to have managed both Data Scientists and ML Engineers, but you should have enough fluency in each to set direction and earn the team's trust
• Track record of delivering ML or DS work that reached production and created measurable business or product impact
• Strong grounding in experimentation (A/B testing), measurement, and impact evaluation
• Experience with applied problems such as personalisation/recommendation, optimisation, pricing, or forecasting
• A pragmatic, current view on AI its maturity, where it adds value, and how to integrate it responsibly into a team's workflow
• Strong stakeholder and communication skills — able to influence Product, Engineering, and business leaders
• Comfort operating in a fast-paced, evolving environment with a high degree of ownership
• Fluent in English, with excellent written and verbal communication skills

Our values

• We win together: Food waste is a big beast to fight. We believe in a #oneteam.
• We raise the bar: We always push for more. We work smart, smash barriers and elevate one another.
• We keep it simple: Our ambitions are bold but our solutions are simple.
• We build a legacy: We're proud of the change we're driving.
• We care: We always look out for each other. Caring is also about the way we do business. We do the right thing.

What we have to offer

• An opportunity to work in a global social-impact company and certified B Corporation, where you can see a real and tangible impact in your role.
• The chance to build and lead a centralised data science and ML capability from an early stage, shaping how advanced methods drive decisions across the company.
• Work-life balance is important to us! Focus on the job to be done, not the hours spent — there is no need for overtime.
• We trust our employees and encourage an autonomous environment with several opportunities to contribute, develop, and take ownership.
• To be part of an international company, with over 1,200+ colleagues across 20 countries on the same important mission.
• An informal environment, working alongside 170+ passionate people within Tech & Product.
• We celebrate our differences and our successes and have a strong values-driven team culture.
• We work in a hybrid set-up (4 days per week in our Copenhagen office) and value in-office work weekly.
• We may be able to support you with a working visa in Copenhagen, please be prepared to discuss this topic further at your first-stage interview.

How to apply

• We take recruitment very seriously, so please carefully read everything we have written above. Please also check our website and international media to get a good overview of Too Good To Go.
• Submit your CV in English.
• Please note that we only accept applications coming through our platform. No CV will be accepted by email or LinkedIn direct messaging.