Junior Data Scientist

Descripción del puesto:

Company Description:

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 EUR850M, 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 EUR1 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.

Job Description:

We are looking for a Junior Data Scientist to join the CDAIO team within a banking client in their Corporate & Investment Banking area 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 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.

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