Data Scientist
Too Good To Go
| Company | Too Good To Go |
| Category | Data & Analytics |
| Location | København |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 30 Mar 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
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 133 million registered users and 261,000 active partners across 21 countries, have together already prevented 517+ million meals from going to waste - avoiding over 1.397.000 tonnes of CO2e!
Your mission
We are looking for a Data Scientist to join our Product Analytics and Center of Excellence team at Too Good To Go.
In this role, you will help build and scale data science capabilities across product and business domains , enabling smarter, more automated, and more impactful decision-making.
As a marketplace, many of our most critical challenges revolve around supply-demand dynamics, user behavior, and marketplace efficiency . You will work on problems such as personalisation, optimisation, and forecasting , helping us move from reactive analysis to proactive, model-driven systems .
You will work as part of the Center of Excellence , partnering closely with Product, Analytics, and business teams through high-impact, project-based collaborations .
Your role
Partner with Product and business leaders to identify high-impact opportunities where data science can drive step-change improvements
Develop and deploy machine learning models and statistical methods to solve problems such as: Personalisation and recommendation systems, Marketplace optimisation (e.g. ranking, matching, supply-demand balance) and Demand and supply forecasting.
Translate ambiguous business problems into scalable models, algorithms, and data products
Design and contribute to experimentation and measurement frameworks to evaluate impact
Collaborate closely with Product Analysts, ML Engineers, and stakeholders within the Product Analytics and Center of Excellence , working in an embedded, project-based model
Take end-to-end ownership , from problem framing to model development and impact evaluation
Contribute to building data science best practices, tools, and standards across the company
Requirements
Master’s degree or PhD in Data Science, Statistics, Machine Learning, Econometrics, or a related field
Strong experience applying machine learning and statistical modeling to product or business problems
Experience in at least one of the following areas: Personalisation / recommendation systems, Marketplace or optimisation problems, Forecasting (time series or causal)
Strong programming skills in Python and solid proficiency in SQL
Experience with experimentation (A/B testing) and impact measurement
Ability to operate in ambiguous problem spaces and translate them into structured solutions
Strong communication skills — able to influence product and business stakeholders
High ownership and comfort working in a fast-paced, evolving environment
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