Data Scientist

Who You'll Work With

You will work with other data scientists, data engineers, machine learning engineers, designers, and project managers on interdisciplinary projects, using math, stats, and machine learning to derive structure and knowledge from raw data across various industry sectors.

You are a highly collaborative individual capable of setting aside your own agenda, listening to and learning from colleagues, challenging thoughtfully, and prioritizing impact. You seek ways to improve processes and work collaboratively. You believe in iterative change, experimenting with new approaches, learning, and improving to move forward quickly.

Your Impact

At McKinsey, you will work on real-world, high-impact projects across various industries. You will collaborate with QB/Labs teams to build complex and innovative ML systems, accelerating AI work and solving business problems at speed and scale.

This environment fosters growth as a technologist and leader. You will develop a perspective connecting technology and business value by working on real-life problems across industries and technical challenges to serve our clients' evolving needs.

You will be part of diverse, multidisciplinary teams, developing a holistic perspective of AI by partnering with top design, technical, and business talent worldwide.

While we advocate using the right technology for each task, common tools include Python, PySpark, the PyData stack, SQL, Airflow, Databricks, Kedro, Dask/RAPIDS, Docker, Kubernetes, and cloud solutions like AWS, GCP, and Azure.

As a Data Scientist, you will

1. Partner with clients—from data owners to C-level executives—to understand needs and build impactful analytics solutions.
2. Contribute to cross-functional problem-solving sessions and deliver presentations to colleagues and clients.
3. Translate business problems into analytical problems and develop models evaluated with relevant metrics.
4. Write optimized code to enhance our Data Science Toolbox.
5. Apply academic expertise to real-world impact; opportunities include writing papers and presenting at conferences.
6. Participate in R&D projects and attend conferences like NIPS and ICML, sharing and learning with colleagues.
7. Work within one of the most advanced data science teams globally.
8. Develop frameworks and libraries used by data scientists and engineers to turn data into impact.
9. Guide global companies through data science solutions to transform businesses across healthcare, automotive, energy, and sports sectors.

Your Qualifications and Skills

1. Bachelor's, master's, or PhD in computer science, machine learning, applied statistics, mathematics, engineering, or AI.
2. 2+ years of professional experience applying machine learning and data mining to real problems with large data sets.
3. Proficiency in programming, especially SQL and Python’s Data Science stack; knowledge of big data frameworks (PySpark, Hive, Hadoop) is a plus; R, SPSS, SAS are nice to have; Software Engineering skills are a plus.
4. Ability to prototype statistical analyses and models, applying algorithms to data-driven solutions in new domains.
5. Experience deploying technology solutions to business problems is advantageous.
6. Knowledge of applying machine learning to complex or large data sets.
7. Knowledge in prompt engineering and Generative AI applications (RAG, Agentic systems), with a basic understanding of engineering standards and QA/Risk Management.
8. Willingness to travel.