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# Expert Data Scientist - Customer & Decision Intelligence 1

- **Company:** Media Gmbh
- **Location:** Ingolstadt, Germany
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, BigQuery, Computer Programming, Integer Programming, Python (Programming Language), Machine Learning, NumPy, Recommender Systems, Markdown, Tensorflow, Retail Software, SciPy, SQL Databases, Unstructured Data, Feature Store, Google Cloud, Pytorch, Large Language Models, Deep Learning, Generative AI, Scikit Learn, Information Technology, Statistics Packages, Xgboost, Machine Learning Operations, Data Pipelines
- **Published:** October 25, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0aaad1ea1ef01692

## About the Role

* Master's degree (MSc) or PhD from an established, internationally recognized university in Mathematics, Statistics, Physics, Computer Science, Data Science, Operations Research, or a closely related quantitative field.
 * Approximately 4-5 years of relevant professional experience after graduation in data science, statistical modeling, applied mathematics, or machine learning.
 * A strong foundation in probability theory, statistical inference, linear algebra, numerical methods, and mathematical optimization, with the ability to explain the assumptions and limitations of different models.
 * Proven ability to compare classical statistical models, optimization methods, and machine learning approaches and justify the choice using scientific reasoning and evidence.
 * Practical experience in developing and validating predictive and optimization models; experience in operations intelligence (forecasting, inventory, supply chain, and pricing) or customer intelligence (recommendation systems, personalization, and CRM analytics) is an advantage.
 * Experience in e-commerce, marketplaces, digital products, media, retail technology, or customer-facing systems is an advantage.
 * Excellent programming skills in Python and SQL, with experience processing large-scale structured and unstructured datasets.
 * Experience with scientific Python libraries (e.g., NumPy, SciPy, statsmodels, and scikit-learn) and ML frameworks such as XGBoost, LightGBM, TensorFlow, or PyTorch.
 * Familiarity with Google Cloud Platform (GCP), including BigQuery, Vertex AI, and modern MLOps practices, is an advantage.
 * Understanding of modern AI methods, including deep learning and Generative AI / LLMs, and the judgment to identify when these approaches are-or are not-appropriate.
 * Strong analytical and problem-solving skills with the ability to translate business needs into sound mathematical and statistical solutions.
 * Excellent communication and stakeholder management skills, with the ability to explain complex concepts to both technical and business audiences.
 * A collaborative mindset, scientific curiosity, intellectual independence, and a pragmatic approach to delivering measurable business impact.

## Description

* Deliver measurable commercial and operational impact through operations intelligence, customer intelligence, and personalization.
 * Contribute to the development of the Decision Science roadmap for operations intelligence and customer intelligence by applying scientifically sound statistical modeling, mathematical optimization, and machine learning.
 * Translate business questions into well-defined mathematical and statistical problems, critically assess the available data, and select and justify the most appropriate modeling approach.
 * Operations Intelligence: develop models for demand forecasting, inventory and replenishment optimization, assortment and allocation, supply chain and logistics optimization, workforce and store capacity planning, and pricing and markdown optimization.
 * Customer Intelligence: develop recommendation systems and models for personalization, customer segmentation, customer lifetime value, churn, propensity, marketing effectiveness, and uplift.
 * Choose methods based on assumptions, interpretability, robustness, computational efficiency, and empirical performance: use a simple linear model when sufficient, and advanced ML or AI only when it adds demonstrable value.
 * Build predictive, prescriptive, and optimization models (e.g., linear/integer programming, simulation, causal inference, experimentation) to improve business performance and operational efficiency.
 * Collaborate with Data Engineers and ML Engineers to build scalable data pipelines, feature stores, and production-ready ML solutions using Google Cloud Platform (BigQuery, Vertex AI).
 * Evaluate, monitor, and continuously improve model performance through baseline comparisons and the validation of assumptions, uncertainty estimates, and generalization, while ensuring business value.