Data Scientist (Risk)

We are seeking a highly skilled and motivated Data Scientist to join our Data Science stream in the Risk Department. In this role, you will work on both operational model lifecycle tasks and next-generation ML initiatives, including hybrid modeling, behavioral modeling, and automation of our ML workflows. You’ll work in a dynamic and fast-paced environment, contributing to projects that directly impact our risk decisioning capabilities and future ML stack.

Key Responsibilities:

• Design, develop, and maintain risk models using advanced machine learning techniques, including hybrid and sequential approaches.

• Regularly retrain, monitor, and validate existing models to ensure timely adaptation to changing customer behavior.

• Build and manage scalable data marts in BigQuery to support model development and analytical needs.

• Contribute to development of internal automation tools (e.g., AutoML components, ML workflows).

• Provide data-driven insights through ad-hoc analyses to support product, credit, and operations teams.

• Participate in early research and prototyping of new architectures (Transformers, foundation-style embeddings, multimodal models).

What you’ll bring:

• At least 2 years of experience in roles such as Data Scientist, ML Engineer, or Risk Analyst, with a proven track record of impactful contributions in credit scoring.

• Proficiency in Python and experience with key data science and machine learning libraries (e.g., NumPy, Pandas, scikit-learn, CatBoost, XGBoost, LightGBM).

• Strong knowledge of PyTorch, with the ability to implement and fine-tune complex machine learning models.

• Advanced knowledge of SQL, with the ability to work effectively with large datasets.

• Experience in building and deploying end-to-end machine learning solutions that drive measurable business impact.

• Hands-on experience with tools like Airflow and Docker for deploying machine learning models into production.

• Fluency in English

Nice to have:

• Familiarity with cloud platforms such as AWS, Google Cloud Platform (GCP), or Microsoft Azure for scalable data analysis and model deployment.

• Proficiency in visualization tools like Tableau or Power BI to effectively communicate insights and support decision-making.

• Prior experience working with financial datasets and a strong understanding of risk management principles.

What we offer:

• Full-time B2B contract

• Fully remote setup

• Up to 20% tax allowance

• 22 paid leave days annually

• Stock options (ESOP) in a fast-scaling, pre-IPO company

• Flexi benefits you can use for wellness, travel, or learning

• Work alongside a high-performing, international engineering team in a global fintech unicorn