Über data-talent.de suchen wir für einen führenden europäischen Omnichannel/E-Commerce Anbieter mit circa 1.200 Mitarbeitern, 1,7 Millionen aktiven Kund:innen, und einem Jahresumsatz von circa 800 Millionen Euro in München eine:n erfahrene:n Datenwissenschaftler:in / Data Scientist (m/w/d) — in unbefristeter Festanstellung mit einem Gehalt von bis zu 88.000 € (inkl. Bonus) plus 13 Gehälter, 30 Urlaubstage, und einem umfangreichen Benefits-Paket. Diese Schlüsselrolle entwickelt und optimiert datenbasierte Prognosemodelle, Machine-Learning-Lösungen in Python oder R, führt A/B-Tests und kausale Analysen durch, und steigert Kundenbindung, Customer Lifetime Value sowie Conversion in enger Zusammenarbeit mit Data- und Produktteams.
About This Data Scientist Opportunity — Führender Omnichannel/E-Commerce Anbieter München
Company: Ein führender Anbieter in Europa im Bereich Omnichannel/E-Commerce — mit circa 1.200 Mitarbeitern, mehr als 1,7 Millionen aktiven Kund:innen, und einem Jahresumsatz von circa 800 Millionen Euro — ein innovatives, dynamisches Unternehmen in dem Data Science und Machine Learning direkte Geschäftsrelevanz haben
Role: Datenwissenschaftler:in / Data Scientist (m/w/d) — Schlüsselrolle bei der Analyse und Interpretation von Daten zur Gewinnung wertvoller Erkenntnisse und zur Weiterentwicklung der Geschäftsstrategie im E-Commerce-Umfeld
Contract: Unbefristete Festanstellung — mit einem Gehalt von bis zu 88.000 € (Bonus inkludiert), 13 Gehältern, 30 Urlaubstagen, und einem umfangreichen Benefits-Paket
Contact: Markus Grossmann — markus@data-talent.de | Tel: 01739504928 | data-talent.de/jobs
Why This Data Scientist Role in München Stands Out
Bis zu 88.000 € + 13 Gehälter + 30 Tage Urlaub: One of the most attractive Data Scientist compensation packages in Munich’s competitive analytics talent market — 13 monthly salaries, 30 days holiday, bonus included, betriebliche Altersvorsorge, Berufsunfähigkeitsversicherung, and a comprehensive health programme with weekly yoga and massage
Real Commercial Impact — 1,7 Millionen Kund:innen: Work on Customer Analytics, Customer Lifetime Value, Kundenbindung, and Conversion models that are actively used to drive decisions for 1.7 million real customers — not academic exercises or POC projects but live ML solutions that generate measurable commercial outcomes at €800 million revenue scale
Full Data Science Lifecycle — Prognose bis Produktion: From Prognosemodell development and Feature Engineering through A/B Testing and causal analysis to integration of scalable ML models into productive systems — a genuinely full-cycle Data Science role in collaboration with Data Engineering, BI, and product teams
Hybrid München — 50% Remote: 2 days per week on-site in Munich, the rest mobile — the professional flexibility of a hybrid role combined with the collaborative energy of a Munich Omnichannel company’s data team, with a subsidised canteen, Spendit card, and comprehensive health programme on-site
Position Overview — Datenwissenschaftler:in / Data Scientist (m/w/d)
This Datenwissenschaftler:in / Data Scientist (m/w/d) in München develops and advances data-based forecasting models (Prognosemodelle) for customer behaviour analysis and Customer Journey optimisation in an E-Commerce environment — develops and implements Machine Learning solutions using Python or R with statistical analyses and Feature Engineering — plans, executes, and evaluates A/B tests and causal analysis procedures — builds and optimises analytical models to sustainably increase Kundenbindung, Customer Lifetime Value, and Conversion — develops predictive, diagnostic, and decision-support models — works closely with data and product teams to translate analytical insights into concrete business decisions — and cooperates with Data Engineering and BI teams to integrate scalable ML models into productive systems and analytical platforms.
Why This Role Matters: As Data Scientist at this Munich Omnichannel/E-Commerce company, the models you build and the analyses you produce determine whether 1.7 million active customers are presented with the right product, offer, or communication at the right point in their Customer Journey — at the scale and commercial consequence that €800 million annual revenue makes very concrete indeed. When your Customer Lifetime Value model correctly identifies the segment of early-lifecycle customers who are statistically most likely to become high-value long-term buyers and the marketing team acts on that signal to increase investment in the right acquisition channels, your A/B test correctly determines that a product recommendation algorithm variant actually increases Conversion by 3.2% over the control and not by the 8% that the stakeholder intuited, or your Kundenbindungsmodell correctly predicts churn risk for a segment of subscribers two months before the expected renewal decision — giving the CRM team enough lead time to intervene effectively — you are not performing statistical analysis. You are directly contributing to the commercial performance of one of Europe’s leading E-Commerce platforms. That is the professional consequence and genuine business impact of a well-executed Data Scientist role at this scale.
Key Responsibilities — Deine Aufgaben
Prognosemodelle, Machine Learning & Feature Engineering
- Develop and advance data-based forecasting models (datenbasierte Prognosemodelle) for customer behaviour analysis and Customer Journey optimisation — applying statistical modelling and Machine Learning to produce the predictive analytical capabilities that allow this European E-Commerce leader to anticipate customer needs, preferences, and lifecycle stages with the accuracy and reliability that meaningful commercial personalisation requires
- Develop and implement Machine Learning solutions using Python or R — conducting statistical analyses and developing meaningful Features (Feature Engineering) for complex customer datasets, applying supervised and unsupervised learning methods with the technical discipline and domain awareness that reliable, production-ready ML in an E-Commerce context demands
- Apply proficiency in scikit-learn, pandas, statsmodels, and other relevant Data Science libraries — with experience in productive Machine Learning applications, Modellvalidierung, and quality assessment that goes beyond model building to ensuring that deployed models continue to perform reliably in the live E-Commerce environment
A/B Testing, Kausalanalyse & Experimentdesign
- Plan, execute, and evaluate experiments — including A/B tests and causal analysis methods (kausale Analyseverfahren) — to validly assess the impact of business measures on customer behaviour, conversion, and engagement at a company where the customer base and transaction volume are large enough to generate statistically meaningful results from well-designed experiments
- Apply experimental design and causal inference methodology — developing the analytical rigour and statistical judgment to distinguish genuine causal effects from correlation, confounding, and novelty effects in the complex, multi-variable customer behaviour data that large-scale E-Commerce operations generate
- Translate experimental findings into concrete business decisions — working closely with marketing, product, and commercial teams to communicate A/B test and causal analysis results in terms of business implications, actionable recommendations, and validated impact estimates that non-technical stakeholders can understand and act on
Customer Analytics, CLV, Kundenbindung & Conversion
- Build and optimise analytical models with the goal of sustainably increasing Kundenbindung (customer retention), Customer Lifetime Value, and Conversion — developing the predictive and diagnostic models that allow this E-Commerce company to move from reactive customer management to proactive, data-driven customer relationship strategy at 1.7 million customer scale
- Develop predictive, diagnostic, and decision-support models that identify relationships in customer behaviour and deliver reliable prognoses for business questions — applying the full repertoire of customer analytics methodology including churn prediction, propensity modelling, customer segmentation, recommendation system inputs, and basket analysis
- Cooperate with Data Engineering and BI teams for integration of scalable Machine Learning models into productive systems and analytical platforms — contributing the data science expertise and model specification that Data Engineering teams need to deploy ML outputs reliably and scalably in the E-Commerce technology stack
Requirements & Qualifications — Was Du mitbringen solltest
Essential Requirements
- Successfully completed degree in Data Science, Statistics, Computer Science, Business Informatics, or a comparable quantitative programme with a focus on Data Analytics or Machine Learning
- Solid practical experience in the development of statistical models and the use of Python or R for data analysis and Machine Learning applications — applied to real commercial data problems
- Confident understanding of supervised and unsupervised learning methods — with experience in Feature Engineering, Modellvalidierung, and quality assessment for production ML applications
- Good knowledge of common Data Science libraries including scikit-learn, pandas, and statsmodels — with experience in productive Machine Learning applications beyond prototyping
- Demonstrated practice in Customer Analytics and data-driven decision processes — ideally in E-Commerce, retail, or trade environments where customer behaviour data at scale is the primary analytical resource
- Strong analytical skills with experience in Kundenwertanalyse, Kundenbindung, and experimental analysis methods including A/B testing and causal inference
- Very good English skills — ideally also German — and genuine enjoyment of interdisciplinary collaboration in agile teams and translating complex analyses into concrete business success
- Willingness to be regularly present in the Munich office — up to two days per week
About This Data Science Opportunity — E-Commerce at European Scale
This Datenwissenschaftler:in / Data Scientist position in München is offered by data-talent.de on behalf of one of Europe’s leading Omnichannel/E-Commerce providers — a company with approximately 1,200 employees, more than 1.7 million active customers, and approximately €800 million in annual revenue — where Data Science and Machine Learning are not support functions but commercial engines that directly drive customer acquisition, retention, lifetime value, and conversion performance across one of Germany’s most sophisticated E-Commerce platforms. With a salary of up to €88,000 including bonus, 13 monthly salaries, 30 days holiday, 50% remote working, weekly yoga and massage, betriebliche Altersvorsorge, Berufsunfähigkeitsversicherung, childcare allowance, subsidised canteen, and Spendit card — this is a genuinely outstanding Data Scientist opportunity in Munich for a Python/R-proficient, customer analytics-experienced, ML-capable data professional ready to create real commercial impact at real scale. Apply with CV, salary expectation, and earliest start date to Markus Grossmann at markus@data-talent.de or call 01739504928.
Career Excellence: Prognosemodelle, Machine Learning, Customer Analytics, A/B Testing, Customer Lifetime Value — Datenwissenschaftler:in (m/w/d), München Bavaria Germany, bis zu 88.000 €, 13 Gehälter, unbefristet 2026.
Who Should Apply?
- Customer Analytics Data Scientists — E-Commerce München: With Python or R proficiency, scikit-learn/pandas/statsmodels expertise, and demonstrated Customer Analytics experience — ideally in E-Commerce, retail, or trade — seeking an unbefristete Festanstellung in Munich with up to 88.000 € salary, 13 Gehälter, and a genuinely comprehensive benefits package at a leading European Omnichannel platform
- Machine Learning Engineers — Prognosemodelle & CLV: With supervised and unsupervised ML experience applied to customer behaviour, Customer Lifetime Value, or churn modelling — ready to develop and deploy predictive and decision-support models in a production E-Commerce environment with 1.7 million active customers
- A/B Testing & Kausalanalyse Specialists — Data Science Germany: With experimental design, A/B testing, and causal inference methodology experience — applied to the kind of complex, multi-variable customer behaviour questions that large-scale E-Commerce generates and where rigorous experimental analysis determines whether business decisions are actually evidence-based
- Data Scientists — Feature Engineering & Model Deployment: With end-to-end data science experience from Feature Engineering and model development through Modellvalidierung and cooperation with Data Engineering teams on production ML integration — seeking a role where the data science work has direct, measurable commercial impact at meaningful scale
- München-Based Data Scientists — Hybrid Remote & Attraktives Gehalt: Seeking a Datenwissenschaftler:in position in Munich with a hybrid (50% remote) working arrangement, up to 88.000 € including bonus, 13 Gehälter, 30 days holiday, weekly yoga and massage, Berufsunfähigkeitsversicherung, betriebliche Altersvorsorge, and childcare allowance — at one of Europe’s leading E-Commerce and Omnichannel companies
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