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.

• Continuous learning: Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast-paced learning experience, owning your journey.
• A voice that matters: From day one, we value your ideas and contributions. You’ll make a tangible impact by offering innovative ideas and practical solutions, all while upholding our unwavering commitment to ethics and integrity. We not only encourage diverse perspectives, but they are critical in driving us toward the best possible outcomes.
• Global community: With colleagues across 65+ countries and over 100 different nationalities, our firm’s diversity fuels creativity and helps us come up with the best solutions for our clients. Plus, you’ll have the opportunity to learn from exceptional colleagues with diverse backgrounds and experiences.
• World-class benefits: On top of a competitive salary (based on your location, experience, and skills), we provide a comprehensive benefits package to enable holistic well-being for you and your family.

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...)