Talan is an international consulting group specializing in innovation and business transformation through technology. With over 7,200 consultantsin 21 countriesand 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 empowermentto 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 revenuethis 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 initiativesthrough 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 DescriptionWe are looking for aJunior Data Scientistto join theCDAIOteam within a banking client in theirCorporate & Investment Banking area inMadrid. You will contribute to the development, validation, and industrialization ofMachine Learning and AI modelsapplied to high-impact business problems.
This is a great opportunity to work in anexciting, fast-evolving AI environment, where modern modeling techniques are actively helping totransform 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 deliverrobust, explainable, and scalablesolutions—covering the full lifecycle from problem framing to deployment and monitoring.
What you’ll do
- Develop/apply state-of-the-artstatistical, machine learning, and AI models(supervised/unsupervised, forecasting, NLP, anomaly detection, etc.) for use cases.
- Performdata exploration, feature engineering, and model evaluationusing rigorous quantitative approaches.
- Apply best practices inmodel validation: cross-validation, bias/variance diagnostics, calibration, robustness testing, and sensitivity analysis.
- Implement and maintainreproducible ML pipelines(training, inference, monitoring) with strong software engineering standards.
- Contribute toexplainability 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.
Must-have Requirements:
- Bachelor’s or master’s degree (or final-year student) inComputer Science, Mathematics, Statistics, Physics, Engineering, or related quantitative field.
- Strong foundations inlinear algebra, probability, statistics, optimization, and numerical methods.
- Solid programming skills inPython(clean code, testing mindset, packaging basics).
- Hands‑on experience with ML libraries such asscikit-learn, and familiarity with at least one deep learning framework (PyTorchorTensorFlow).
- Practical knowledge ofmodel evaluationand metrics (AUC, precision/recall, RMSE, calibration, etc.) and experimentation methodology.
- Experience working with data usingpandas/numpy, and querying withSQL.
- Good communication skills and ability to work in collaborative, cross‑functional teams.
- Professional working proficiency inEnglish and Spanish
Nice to have
- Previous experience in similar roles.
- Exposure toNLP(transformers, embeddings),LLMs, orgenerative AIconcepts (prompting, fine‑tuning basics, retrieval).
- Understanding ofMLOpsconcepts and tools (e.g., MLflow, Docker, CI/CD, model monitoring).
- Experience withcloud platforms(AWS/Azure/GCP) and distributed processing (e.g., Spark).
- Familiarity withDatabricks(or willingness to learn it on the job) for collaborative development and scalable ML workflows.
- Familiarity withtime series modeling, stress testing, or causal inference.
- Interest or exposure toCorporate & Investment Banking / Global Bankingproducts 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 withFinance analyticsconcepts such asP&L drivers,balance sheet metrics,FTP,capital/RWA, or management reporting—able to translate financial KPIs into modeling objectives.
- Understanding ofRiskfundamentals (credit risk, market risk, liquidity risk, operational risk) and common modeling topics such asPD/LGD/EAD,rating/scorecards,stress testing,early warning signals, orportfolio monitoringin a regulated environment.
What do we offer you?
- Hybridposition based inMadrid, Spain
- Permanent, full‑time contract.
- Smart Office Pack so that you can work comfortably from home.
- Training and career development.
- Benefits and perks such asprivate 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 !
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