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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - McKinsey Transformation - **Company:** McKinsey & Company - **Location:** Brussel, Belgium - **Contract:** Permanent contract - **Skills:** Artificial Neural Networks, Software as a Service, Cloud Database, Code Review, Computer Programming, Continuous Integration, Cursor (Graphical User Interface Elements), Software Debugging, Github, Python (Programming Language), Machine Learning, Tensorflow, Software Configuration Management, SQL Databases, Computational Statistics, Support Vector Machine, Tableau (Software), Unstructured Data, Pytorch, Large Language Models, Random Forest, Prompt Engineering, Deep Learning, Generative AI, Git, Pyspark, Information Technology, Optimization Algorithms, Xgboost, Machine Learning Operations, Software Library, Databricks - **Published:** June 6, 2026 - **Apply:** https://be.indeed.com/viewjob?jk=9ccefa11637b4b16 ## About the Role Do you have a Master's degree?, MSc or PhD level in the field of computer science, machine learning, applied statistics, mathematics or equivalent by experience 1+ years of experience in data science, statistical modelling (e.g. advanced regressions, clustering, classification models) and machine learning techniques (e.g. random forest, support vector machines, gradient boosting, XGBoost) Programming experience in the following languages: Python and SQL. Basic understanding of PySpark Comfortable leveraging the latest GenAI tools (such as Cursor or Claude) to accelerate coding, debugging, and problem-solving. Experience with version control using Git and collaborative development workflows in GitHub (e.g., branching, pull requests, code reviews, and repository management) Experience collaborating on projects to deploy advanced analytics and data science methods in real-world organizations (CI/CD, deployment observation, etc...) Familiarity with LLMs agents, RAG systems, prompt engineering, machine learning libraries (scikit-learn) or deep learning libraries (TensorFlow/PyTorch) is a plus Exposure to tools like Databricks and Tableau is a plus Effective communication skills, including a readiness to break down analytical concepts, methods, and results for non-technical stakeholders. (e.g. consultants, etc...) ## Description Do you want to do work that matters, alongside supportive leaders who will help you grow faster than you ever thought possible? Are you a creative problem-solver who is energized by challenges? You've come to the right place. YOUR IMPACT Working closely with a cross-functional team of data scientists, data engineers, product developers, and analytics-focused consultants, your core responsibility involves collaborating with product management to build performant, robust, and maintainable analytics products that adhere to established data-science approaches and best practices. You will expand your expertise across various analytics topics, encompassing descriptive analytics as well as the design, development, and refinement of statistical models, optimization techniques, advanced machine learning, and predictive models like boosted trees. Furthermore, driving the fine-tuning and evaluation of LLMs requires leveraging the latest advancements in neural network architectures and machine learning techniques, optimizing their performance and efficiency particularly for segmentation use cases. Accelerating this development and problem-solving process involves utilizing cutting-edge GenAI tools such as Claude and Cursor, alongside implementing efficient development and maintenance workflows (MLOps) within our Databricks platform. Translating these complex data analyses into actionable analytical insights directly guides client project directions, while simultaneously fueling your contributions to internal knowledge sharing, research, and technical documentation focused on unstructured data, LLMs, and predictive modeling. Ultimately, all these efforts converge to bring advanced analytics capabilities into one of the firm's flagship products, "Wave" serving as the backbone for how McKinsey runs future transformations and utilizing powerful data science assets to dramatically improve the odds of success for our clients. You will work in our McKinsey Client Capabilities Network in EMEA and will be part of our Wave Transformatics team. Wave is a McKinsey SaaS product that equips clients to successfully manage improvement programs and transformations. Focused on business impact, Wave allows clients to track the impact of individual initiatives and understand how they affect longer term goals. It is a mix of an intuitive interface and McKinsey business expertise that gives clients a simple and insightful picture of what can otherwise be a complex process by allowing them to track the progress and performance of initiatives against business goals, budget and time frames. Our Transformatics team builds data and AI products to provide analytics insights to clients and McKinsey teams involved in transformation programs across the globe. The current team is composed of 3 data engineers, 5 data scientists,1 analyst and 2 PMs who are spread across several geographies. The team covers a variety of industries, functions, analytics methodologies and platforms - e.g. Cloud data engineering, advanced statistics, machine learning, predictive analytics, MLOps and generative AI. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)