Senior Data Scientist - Germany - Remote
We are seeking a skilled and curious Data Scientist (m/f/d) to join a growing analytics and AI team.
You will drive data-driven decision-making and develop machine learning models that solve complex business challenges.
From data exploration and modeling to deployment and monitoring, you’ll be involved in the full data science lifecycle.
Key Responsibilities
- Analyze structured and unstructured data to extract actionable insights and support strategic decisions.
- Design, develop, and evaluate predictive models, classification systems, recommendation engines, or forecasting algorithms.
- Collaborate with product managers, software engineers, and business stakeholders to translate problems into data solutions.
- Visualize results through dashboards and presentations (e.g., using Power BI, Tableau, or Plotly).
- Develop reproducible pipelines for data processing and model training using tools like SQL, Pandas, Scikit-learn, TensorFlow, or PyTorch.
- Deploy and monitor models in production with the support of MLOps/engineering teams (if applicable).
- Maintain documentation and ensure your work is reproducible, explainable, and aligned with data privacy regulations like GDPR.
Requirements
- PhD in Data Science, Computer Science, Mathematics, Statistics, or a related field.
- 5+ years of experience in applied data science or machine learning.
- Strong programming skills in Python or R.
- Solid experience with SQL and working with relational databases.
- Proficiency in common ML and statistical modeling libraries (e.g., Scikit-learn, XGBoost, TensorFlow).
- Familiarity with version control (e.g., Git), and experience working in collaborative coding environments.
- Fluent in English (business level); German is a plus.
Preferred Qualifications
- Experience with cloud platforms (AWS, GCP, or Azure).
- Familiarity with MLOps tools (MLflow, Airflow, Docker).
- Background in a domain like finance, e-commerce, healthcare, or manufacturing.
- Experience with A/B testing, uplift modeling, or causal inference.