Senior Applied Scientist - Machine Learning

Position Overview

We are seeking a Senior Applied Scientist skilled in Machine Learning to join our team at Mecklenburgische Versicherungs-Gesellschaft a. G. in Coburg, Bavaria, Germany. This role involves designing, developing, and enhancing advanced Machine Learning, Deep Learning, and statistical models to address critical business challenges with significant strategic impact.

Key Duties

• Create and refine complex ML and DL models for predictive analytics, forecasting, and anomaly detection using structured and unstructured data such as texts, documents, and time series.
• Apply modern machine learning techniques including neural networks and transformer-based models on unstructured data.
• Plan, execute, and analyze experiments to formulate and validate data-driven hypotheses supporting business decision-making.
• Translate scientific insights from ML, deep learning, statistics, and optimization into robust, production-ready applications.
• Develop and maintain scalable, reproducible ML/DL pipelines following contemporary MLOps standards emphasizing stability, scalability, and traceability.

Candidate Qualifications

• Completed Master's degree or higher (Ph.D. preferred) in Data Science, Machine Learning, Mathematics, Physics, Economics, Computer Science, or a related field.
• Several years of professional experience as a Data Scientist, Machine Learning Engineer, or in Advanced Analytics, ideally with an emphasis on marketing, customer analytics, or data-driven business models.
• Expertise in machine learning, statistical modeling, predictive analytics, and production-level data science solutions.
• Strong proficiency in Python programming and practical knowledge of state-of-the-art machine learning tools and methodologies.
• Preferred experience with causal inference, uplift modeling, recommender systems, or personalization techniques.
• Problem-solving mindset with an experimental, hands-on approach, high self-motivation, and accountability for project outcomes.
• Willingness to work on-site in Coburg within a hybrid working setup.

Work Arrangement

This is a full-time, on-site role based in Coburg, Bavaria, Germany.