Scientist Data Analytics & Models Online Conciliator at Talan · Madrid

companyDescription Talan – Positive Innovation Talan is an international consulting group specializing in innovation and business transformation through technology. With over 7,200 consultants in 21 countries and a turnover of €850M, we are committed to delivering impactful, future-ready solutions. Talan at a Glance Headquartered in Paris and operating globally, Talan combines technology, innovation, and empowerment to deliver measurable results for our clients. Over the past 22 years, we’ve built a strong presence in the IT and consulting landscape, and we’re on track to reach €1 billion in revenue this year. Our Core Areas of Expertise Data & Technologies: We design and implement large-scale, end-to-end architecture and data solutions, including data integration, data science, visualization, Big Data, AI, and Generative AI. Cloud & Application Services: We integrate leading platforms such as SAP, Salesforce, Oracle, Microsoft, AWS, and IBM Maximo, helping clients transition to the cloud and improve operational efficiency. Management & Innovation Consulting: We lead business and digital transformation initiatives through project and change management best practices (PM, PMO, Agile, Scrum, Product Ownership), and support domains such as Supply Chain, Cybersecurity, and ESG/Low-Carbon strategies. We work with major global clients across diverse sectors, including Transport & Logistics, Financial Services, Energy & Utilities, Retail, and Media & Telecommunications. jobDescription We are looking for a Junior Data Scientist to join the CDAIO team within a banking client in their Corporate & Investment Banking area (SCIB) in Madrid. You will contribute to the development, validation, and industrialization of Machine Learning and AI models applied to high-impact business problems. This is a great opportunity to work in an exciting, fast-evolving AI environment, where modern modeling techniques are actively helping to transform how an investment bank operates—from smarter decision-making and automation to improved risk insights and client-facing capabilities. You’ll collaborate with data scientists, engineers, and business stakeholders to deliver robust, explainable, and scalable solutions—covering the full lifecycle from problem framing to deployment and monitoring. What you’ll do Develop/apply state-of-the-art statistical, machine learning, and AI models (supervised/unsupervised, forecasting, NLP, anomaly detection, etc.) for SCIB 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 Nice to have 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. additionalInformation 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 !