Senior Data Scientist, Growth (f/m/x)
AI in this role
Design and ship statistical and machine learning models for marketing measurement, attribution, and personalization at HelloFresh.
About the role: What's in the Box
The Growth Alliance powers HelloFresh’s marketing engine, from media investment and in-channel optimization, to the conversion funnel, to how and when we communicate with customers. As a Senior Data Scientist, you will design and ship statistical and machine learning models that turn data from experiments, campaigns, and customer behavior into decisions that directly move the business: how we spend our marketing budget, how we convert visitors into customers, and how we personalize every touchpoint along the way.
You’ll join one of several teams within the alliance, spanning marketing measurement and attribution, channel optimisation, funnel optimisation and decisioning on customer communications and messaging. Across all of these topics, the through line is the same: rigorous modeling, close collaboration with engineering and marketing stakeholders, and a bias toward shipping models that work in production, not just in a notebook.
To succeed in this role, you should be a curious, pragmatic problem solver who relentlessly prioritizes based on impact, is comfortable with statistical uncertainty, and can translate a fuzzy business question into a concrete, testable model.
At HelloTech, flexibility and cross-functional collaboration are core to how we work. While this role is aligned to a specific Alliance, strong candidates may also be considered for opportunities across different teams or projects.
What you’ll do: The Recipe
- Design, build, and take ownership of statistical and machine learning models, from data collection through to production, closely aligning the approach with non-technical stakeholders.
- Collaborate within cross-functional teams (engineering, product, marketing) to translate business objectives into concrete, data-driven strategies.
- Experimentation: design tests, define success metrics and guardrails, and interpret results while accounting for statistical uncertainty, noise, and bias.
- Continuously iterate and refine your technical approach, monitor model performance, data reliability, and drift once in production.
- Retrieve, manipulate, and analyze large, heterogeneous datasets, and build the data pipelines your models depend on.
- Think beyond the immediate ask to find new ways to improve the data products delivered to stakeholders.
- As a senior member of the team, jump in to help solve problems as they arise, and support and coach more junior data scientists.
What you’ll bring: The Ingredients
- Proven track record of scientific work through a Bachelor’s, Master’s, or PhD in statistics, physics, economics, mathematics, data science, or a related quantitative field.
- 3+ years of professional experience as a (senior) data scientist or in a related quantitative role, ideally in e-commerce, marketing, or a comparably high-traffic, consumer-facing environment.
- Strong marketing and/or growth domain knowledge, with experience applying statistical models such as regressors and classifiers, and interpreting experiment results under real-world uncertainty.
- Fluency in Python and its scientific stack (NumPy, Pandas, Scikit-learn, Matplotlib); comfortable retrieving and analyzing data with SQL. Experience with Databricks/Spark is a plus.
- Familiarity with software engineering practices and tools (Git, Docker, AWS or similar cloud environments).
- Comfortable using recent Gen AI tools (e.g. Claude Code) to design and build solutions in a structured and pragmatic manner.
- A critical thinker and creative problem solver who is comfortable proposing and prioritizing multiple solutions based on effort and likely impact.
- A collaborative team player with exceptional communication skills, able to work with stakeholders from different backgrounds in an international environment.
Nice to have (domain specific), if you find yourself in one of the below, it’s a huge plus!
- Measurement & attribution: experience with media mix models, attribution modeling, or incrementality measurement; Bayesian forecasting and estimating model uncertainty.
- Search or Social channel optimization: experience with the Google/Meta ecosystem or other paid channels, forecasting algorithms, portfolio optimization, or Vector Autoregression.
- Conversion & personalization: experience building decisioning or recommendation models that run in production behind high-traffic conversion funnels; customer segmentation and behavioral data.
- Messaging & send decisioning: direct experience with reinforcement learning or contextual bandits (e.g. Thompson Sampling, epsilon-greedy), off-policy/counterfactual evaluation, or CRM and marketing decisioning use cases.
What we offer: The Toppings
- Global collaboration at scale: Collaborate with experienced engineers and product partners across HelloTech’s international teams, in a culture of active knowledge sharing.
- Technology with real-world impact: Build and operate modern systems at global scale, supporting 6+ millions of customers and complex supply chain operations.
- Technical/Product/Design leadership: Drive best practices and influence architecture/design, quality, and ways of working in an autonomous, product-led setup.
- End-to-end development/delivery: Drive decisions from problem definition to production, improving systems and enabling long-term scalability.
Are you the missing ingredient? If this sounds like a tasty opportunity, we’d be excited to hear from you. We aim to review your profile and respond within 5 business days.
#DATA
How we rate this
Senior Data Scientist, Growth (f/m/x) at HelloFresh rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where statistical modeling was part of your work. What did you do?
- Tell me about a project where experimentation was part of your work. What did you do?
- Tell me about a project where data pipelines was part of your work. What did you do?
- Tell me about a project where growth modeling was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Machine learning, Statistical Modeling, Experimentation, Data Pipelines, and Growth Modeling. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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