Junior Data Scientist

What we're looking for

• Develop/apply state-of-the-art statistical, machine learning, and AI models (supervised/unsupervised, forecasting, NLP, anomaly detection, etc.) for use cases.
• Perform data exploration, feature engineering, and model evaluation using rigorous quantitative approaches.
• Apply best practices in model validation: cross-validation, bias/variance diagnostics, calibration, robustness testing, and sensitivity analysis.
• Implement and maintain reproducible ML pipelines (training, inference, monitoring) with strong software engineering standards.
• Contribute to explainability and governance (e.g., SHAP, feature attribution, stability, documentation), aligned with a regulated environment.
• Present findings clearly to both technical and non-technical audiences; translate business goals into measurable modeling objectives.
• Stay current with modern AI: deep learning, LLMs, representation learning, and emerging tooling; prototype where relevant.

*Qualifications

Must-have Requirements:**

• Bachelor’s or master’s degree (or final-year student) in Computer Science, Mathematics, Statistics, Physics, Engineering, or related quantitative field.
• Strong foundations in linear algebra, probability, statistics, optimization, and numerical methods.
• Solid programming skills in Python (clean code, testing mindset, packaging basics).
• Hands-on experience with ML libraries such as scikit-learn, and familiarity with at least one deep learning framework (PyTorch or TensorFlow).
• Practical knowledge of model evaluation and metrics (AUC, precision/recall, RMSE, calibration, etc.) and experimentation methodology.
• Experience working with data using pandas/numpy, and querying with SQL.
• Good communication skills and ability to work in collaborative, cross-functional teams.
• Professional working proficiency in **English and Spanish

• Previous experience in similar roles.
• Exposure to NLP (transformers, embeddings), LLMs, or generative AI concepts (prompting, fine-tuning basics, retrieval).
• Understanding of MLOps concepts and tools (e.g., MLflow, Docker, CI/CD, model monitoring).
• Experience with cloud platforms (AWS/Azure/GCP) and distributed processing (e.g., Spark).
• Familiarity with Databricks (or willingness to learn it on the job) for collaborative development and scalable ML workflows.
• Familiarity with time series modeling, stress testing, or causal inference.
• Interest or exposure to Corporate & Investment Banking / Global Banking products and processes (e.g., lending, trade & working capital, DCM/ECM, transaction banking) and how data/AI can support them (client analytics, pricing, limits, early warning).
• Knowledge of model risk / governance in regulated industries (documentation, traceability, controls) is a plus.
• Familiarity with Finance analytics concepts such as P&L drivers, balance sheet metrics, FTP, capital/RWA, or management reporting—able to translate financial KPIs into modeling objectives.
• Understanding of Risk fundamentals (credit risk, market risk, liquidity risk, operational risk) and common modeling topics such as PD/LGD/EAD, rating/scorecards, stress testing, early warning signals, or portfolio monitoring in a regulated environment.

*Additional Information

What do we offer you?**

• Hybrid position based in Madrid, Spain
• Permanent, full-time contract.
• Smart Office Pack so that you can work comfortably from home.
• Training and career development.
• Benefits and perks such as private medical insurance, life insurance, Language lessons, etc
• Possibility to be part of a multicultural team and work on international projects.

*If you are passionate about data, development & tech, we want to meet you !