hellofresh · Berlin, Berlin, Germany
Skills: Python, SQL, AWS, Docker
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 …truncated… mentality 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 recommendat…
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