Graduate

Data Scientist I - QuantumBlack, AI by McKinsey

Competitive

London

TL;DR

Key Takeaways

Develop impactful analytics solutions and optimize code for clients across diverse industries.
Requires a Bachelor's, Master's, or PhD in a technical field and up to 2 years of experience.
Work with cutting-edge technologies including Python, PySpark, and cloud solutions.
Based in London with opportunities for travel.
Competitive salary and comprehensive benefits package.

This role is for a Data Scientist I within QuantumBlack, AI by McKinsey.

Collaborate with clients and interdisciplinary teams to develop impactful analytics solutions, optimize code, and solve real-world business problems across diverse industries.
Contribute to cutting-edge projects, R&D, and global conferences while working alongside world-class talent.
Partner with clients to understand their needs, translate business problems into analytical challenges, build models, and ensure they are evaluated with relevant metrics.
Contribute to internal tools, participate in R&D projects, and attend/present at conferences like NIPS and ICML.
Identify patterns in data and deliver innovative solutions to help clients maintain competitive advantage and transform operations.
Work on interdisciplinary projects using math, statistics, and machine learning to derive insights from raw data.
Help global companies transform their businesses and enhance performance across industries such as healthcare, automotive, energy, and elite sports.
Develop a sought-after perspective by connecting technology and business value, tackle real-life challenges, and collaborate with multidisciplinary teams.

Bachelor’s, Master’s, or PhD degree in Computer Science, Machine Learning, Applied Statistics, Mathematics, Engineering, Physics, or other technical fields.
Up to 2 years of professional experience applying machine learning and data mining techniques to real-world problems with substantial data sets.
Programming experience (focus on machine learning): R and/or Python, with SPSS, SAS, or similar tools considered nice-to-have.
Ability to prototype statistical analysis and modeling algorithms and apply them to develop data-driven solutions in new domains.
Ability to independently own and drive model development while balancing competing demands and deadlines.
Demonstrated aptitude for analytics and a passion for solving complex data challenges.
Experience with Generative AI (GenAI) and agentic systems would be considered a strong plus.
Excellent time management and organizational skills.
Good presentation and communication skills, with the ability to explain complex analytical concepts to people from other fields.
Willingness to travel.
Strong communication skills, both verbal and written, in English, with the ability to adapt to different audiences and seniority levels.

Python, PySpark, the PyData stack, SQL, Airflow, Databricks, Kedro, Dask/RAPIDS, Docker, Kubernetes, and cloud solutions such as AWS, GCP, and Azure.