Senior Data Scientist, Growth (f/m/x)

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...

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 a...

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.
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.

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.
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.