Expert Data Science (m/f/d)

HSE - Home Shopping Europe GmbH; Ismaning; Vor Ort; EUR 60.000 - 90.000; Vollzeit

Zusammenfassung

HSE - Home Shopping Europe GmbH in Germany is seeking a data science professional to advance predictive models and customer analytics for our live commerce platforms. You will apply Python or R, develop ML solutions, perform feature engineering, and validate models.

Collaboration with Marketing, Product, and Engineering will translate insights into measurable business impact, with on-site work two days per week at Ismaning campus.

Qualifikationen

• Degree in data science or related quantitative field with strong ML focus.
• Experience with Python or R for statistical analysis and ML.
• Familiar with ML libraries and model evaluation/validation.

Aufgaben

• Develop predictive models to optimize the customer journey across our e-commerce and TV-shopping platforms.
• Apply ML techniques in Python/R and perform feature engineering and model validation.
• Assist in translating model outputs into concrete business actions with Marketing, Product, and Engineering teams.

Kenntnisse

Statistical analysis

Machine learning

Experimentation frameworks

Ausbildung

Degree in Data Science / Statistics / CS / Business Informatics or related quantitative field

Tools

scikit-learn

pandas

statsmodels

Jobbeschreibung

We are one of Europe’s leading providers in the field of live commerce, offering the most entertaining way to shop as a media platform. With our curated product portfolio spanning fashion, jewelry, beauty, wellness, household, and home & living, we inspire countless viewers, fans, and followers every day across all channels – on TV, in our online shop, and on social media. By seamlessly connecting lifestyle themes and brand worlds, we are driving innovation in cross-channel shopping entertainment. Our customers decide when and where they want to shop with HSE.

What you can expect:

• You develop innovative Predictive Models and Customer Behavior Analytics to optimize the Customer Journey in our dynamic E-Commerce and TV-Shopping environment
• You work with cutting-edge Machine Learning algorithms in Python and R, conduct Statistical Analysis, and create Feature Engineering for complex customer data models
• You implement A/B Testing frameworks, Causal Inference methods, and Statistical Modeling to validate Business Impact and customer behavior patterns
• You optimize Customer Analytics for LTV enhancement, Retention improvement, and Conversion maximization through advanced Data Science techniques
• You create predictive, inferential, and prescriptive models based on diagnostic modelling insights to explain and forecast customer behavior and business outcomes.
• You translate model outputs into measurable business actions in close collaboration with Marketing and Product teams.
• You collaborate closely with Engineering teams to integrate Predictive Analytics models into productive Business Intelligence systems and develop scalable data-driven solutions

Your profile:

• You have a successfully completed degree in Data Science, Statistics, Computer Science, Business Informatics, or comparable quantitative education with strong Machine Learning focus
• You bring solid experience in Statistical Analysis with Python or R and Supervised and Unsupervised Learning techniques for Customer Behavior Predictive Modeling
• You possess practical expertise in Feature Engineering, Model Evaluation, Performance Validation, and know modern Machine Learning libraries like scikit-learn, pandas, and statsmodels
• You have proven experience in productive machine learning models scenarios, Customer Analytics, and Data-driven Decision Making, ideally in E-Commerce or retail environments
• You demonstrate strong analytical thinking for Customer Intelligence, Retention Analytics, and LTV Optimization as well as experience with Experimentation frameworks
• You speak fluent English and ideally German, work solution-oriented in agile Data Science teams, and enjoy translating complex Statistical Analysis into measurable Business Impact
• You are willing to work on site at our campus 2 days per week