* Education: Completed degree (Master/Diploma) in Statistics, Mathematics, Computer Science, Econometrics, Data Science, or a related quantitative field with a strong focus on inferential statistics.
* Experience: You have solid professional experience (approx. 2–5 years) in statistical modeling, ideally with a clear focus on A/B testing or experimentation in a digital/e-commerce environment.
* Tech Stack & Engineering: You are highly proficient in Python and SQL. Applying software engineering best practices (Clean Code, OOP, versioning with Git) is second nature to you. Ideally, you have experience with PySpark and cloud infrastructures (GCP/Azure) or Databricks.
* Methodology: Terms like p-values, confidence intervals, Type-I/II errors, and multivariate tests are part of your daily toolkit. Experience with Sequential Testing or Causal Inference is a significant plus.
* Communication: You have the ability to present complex statistical topics clearly for non-statisticians and enjoy acting as a bridge-builder between Data Engineering and the business units.
* Languages: As we are an international team, you communicate confidently in English. Proficiency in German is a clear advantage for a smooth knowledge transfer during the onboarding phase.
Introduction text
Schwarz Digits creates the technological foundation for digital sovereignty in Europe. As the IT and digital division of the Schwarz Group, we develop and manage the IT infrastructures for the retail divisions Lidl and Kaufland, as well as Schwarz Production and PreZero. At the same time, we operate as an independent provider in the external market to support companies across Europe in their digital transformation. We bundle our core services in the areas of Cloud, Cyber Security, Data & AI, Communication, and Workspace.
Join us and contribute to digital sovereignty in Europe. With us, you will work at the intersection of agility and security: You will benefit from fast decision-making processes, enjoy genuine creative freedom in your projects, and be able to build upon the stable foundation of the Schwarz Group.
With us, you work at the intersection of agility and security: You benefit from fast decision-making processes and enjoy genuine creative freedom in your projects, all while building on the stable foundation of the Schwarz Group.
Within the Data Tech & Enablement department, we shape data-driven decisions along the entire customer journey of our internal client, Lidl Online. Our team takes pride in its methodical depth and technical excellence—and this is where you come in: To take our experimentation culture to the next level and scale our systems for the future, we are looking for a methodologically excellent and technically savvy addition to the team who can seamlessly translate statistical theory into production-ready code.
Your Tasks
* End-to-End Experimentation: You are responsible for the experimental design and statistical evaluation of our A/B tests in the pricing and marketing environment. You will master challenges such as power analysis, randomization strategies, and the methodologically sound interpretation of complex test results.
* Scalable Implementation & Tooling: You don't just act as a consultant; you actively develop. You write production-ready code (Python/SQL), further develop the core statistical functions of our internal A/B testing app, and integrate automated statistical workflows into our cloud data landscape (GCP/Databricks).
* Methodological Lead: You evaluate and implement advanced statistical methods (e.g., sequential analysis or Bayesian approaches) to continuously improve the efficiency and validity of our experiments.
* Statistical Consulting: You are the primary point of contact for Product Managers, Analysts, and Data Engineers regarding statistical questions. You help avoid bias and ensure the validity of our results.
* Knowledge Transfer & Succession: You will play a key role in a long-term, planned generational handover. During a 3- to 6-month onboarding phase, you will work closely with our departing expert to take over deep domain knowledge and lead the team's statistical excellence into the future using modern tech approaches.