Senior Data Scientist

Solaris is Europe’s leading embedded finance platform, pioneering Banking-as-a-Service to bridge the gap between financial technology and licensed banking. Founded in 2016 and headquartered in Berlin, Solaris enables fintechs, digital ecosystems, and multinationals to embed accounts, cards, lending, and payments directly into their own products, creating seamless financial experiences for their users. As a fully licensed German bank, Solaris combines the highest standards of IT security and regulatory resilience with a modular, highly scalable infrastructure. Today, Solaris is driving the next evolution of financial services by transitioning into an AI-native banking platform, ensuring European businesses can deploy secure, adaptable financial products at scale. Why join Solaris? 🚀 We are fundamentally redesigning banking processes around AI orchestration, standardized modular building blocks, and embedded regulatory compliance. 🎯 We combine tech and banking in dedicated hubs - driving the technology infrastructure out of Berlin and banking operations out of Frankfurt. 💡 Through our internal mobility, growth opportunities, and the Solaris Academy, we offer continuous learning tracks, AI ambassador mentorship, and upskilling to keep your skills ahead of the curve. ⚖️ We foster a workplace rooted in integrity, proactive risk management, and equality actively driving initiatives like DEI initiatives. Your Role Development, operationalisation and maintenance of Machine Learning models in close collaboration with the business stakeholders for common risk and financial protection with different latency: Fraud Protection, Compliance & AML Training data preparation in close collaboration with the analytics engineers including analysis of vast amounts of transactional logs, data labelling, applying chronological splitting and sampling techniques to handle class imbalances Feature engineering operations including common features, cross features, positional features and building a centralised feature store Model selection, experimentation and training of baseline and gradient boosted models, evaluating performance and trade offs Model deployment and prediction servicing from batch to online in close collaboration with the data infrastructure team Continual learning by setting up automated pipelines that monitor population drift and continuously re-fit models on fresh data when performance drops below predefined operational baselines. Knowledge sharing and mentoring across the team. We'd love to see Depending on your level of experience, your responsibilities and scope of role will range. We don’t care much about fancy titles, but rather about real personal and professional development, as laid out in our learning framework. Let’s figure together out how you can contribute to our team. Degree in Computer Science, Applied Mathematics, Statistics, Quantitive Finance and targeted Financial Engineering courses. Minimum 6 years experience in a role of data scientist in a fast pace environment and regulated industry. Proficiency in data science libraries (pandas, polars, numpy, scikit-learn) and gradient boosting frameworks (XGBoost). Advanced SQL skills (window functions, query optimisation) and hands on experience in analytical platforms (ideally Snowflake by utilising snowpark). Experience working with centralized feature platforms (e.g., Snowflake Feature Store, Feast, Tecton) to prevent train-serve skew. Good knowledge of data transformation and data orchestration tools, ideally dbt and airflow. Solid understanding of software engineering principles, including version control (Git), CI/CD, and automated testing. Payment, Fraud and Risk Domain Expertise: Understand transactions movement, payment payload and authentication protocols. Recognize differences in typologies, spotting anomalies and understanding chargeback and dispute cycles Velocity Features, Device Dynamics