At a Glance
- Tasks: Design scalable data pipelines and prepare data for advanced analytics in AI projects.
- Company: Join a global leader in data engineering with a diverse and innovative culture.
- Benefits: Competitive salary, comprehensive benefits, and a focus on holistic well-being.
- Other info: Collaborate with top talent in a dynamic environment that fosters growth and learning.
- Why this job: Make a real impact by solving complex business challenges with cutting-edge AI technology.
- Qualifications: Degree in relevant field and 2-5+ years of experience in data solutions.
The predicted salary is between 63000 - 77000 £ per year.
QUALIFICATIONSDegree in Computer Science, Engineering, Mathematics, or equivalent experience2-5+ years of professional experience in building and deploying data solutions Strong coding skills in Python, Scala, or Java, with the ability to write clean, maintainable, and scalable code Proven experience in building and maintaining production-grade data pipelines for advanced analytics use cases Experience working with structured, semi-structured, and unstructured data Hands-on expertise in containerization and orchestration using Docker and Kubernetes for scalable production systems Familiarity with distributed computing frameworks (e. g., AWS, Azure, GCP), and analytics libraries (e. g., pandas, numpy, matplotlib)Exposure to Dev Ops, Data Ops, and MLOps concepts and best practices Experience with core technologies such as Python, Py Spark, SQL, Airflow, Databricks, Kedro, Dask/RAPIDS, Docker, Kubernetes, and cloud services (AWS/GCP/Azure)Experience with Generative AI (Gen AI) or agentic systems is a strong plus Prior client-facing or senior stakeholder management experience is beneficial Excellent time management and communication skills (verbal and written), with flexibility to adapt across audiences; willingness to travel as needed WHO YOU'LL WORK WITHDriving lasting impact and building long-term capabilities with our clients is not easy work.
You are the kind of person who thrives in a high performance/high reward culture - doing hard things, picking yourself up when you stumble, and having the resilience to try another way forward.
Every day, you'll receive apprenticeship, coaching, and exposure that will accelerate your growth in ways you won’t find anywhere else.
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.
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.
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.
WHAT YOU'LL DOAs a Data Engineer II, you will design scalable data pipelines, manage secure data environments, and prepare data for advanced analytics while collaborating with clients and cross-functional teams.
You’ll solve impactful business challenges, contribute to innovative AI projects, and grow as a technologist alongside diverse experts across industries.
In this role, you will architect and build scalable, modular, and reproducible data pipelines for machine learning.
You’ll assess data landscapes, ensure data quality, and prepare data for advanced analytics models.
You’ll also manage secure data environments and contribute to R&D initiatives and internal asset development to expand your technical expertise.
Partnering with Mc Kinsey’s Quantum Black and Labs teams, you’ll help create cutting-edge machine learning systems that accelerate AI adoption and solve business problems at scale—enabling clients to achieve meaningful, lasting impact.
You’ll be based in London as part of our global Data Engineering community.
You’ll work in cross-functional Agile teams alongside Data Scientists, Machine Learning Engineers, and industry experts to deliver advanced analytics solutions.
Collaborating closely with clients—from data owners to C-level executives—you’ll help solve complex problems that drive business value.
Working with inspiring, multidisciplinary teams, you’ll gain a holistic understanding of AI while collaborating with some of the best technical and business talent in the world.
Data Engineer - ML & AI in London employer: Mckinsey & Company
As a Portfolio Manager - Events at our firm, you will thrive in a dynamic and high-performance environment that champions continuous learning and diverse perspectives. We offer exceptional benefits and a global community of colleagues, ensuring your contributions are valued and impactful from day one. With structured mentorship and opportunities for professional growth, you'll be empowered to make a real difference while enjoying a collaborative and innovative work culture.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer - ML & AI in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Mckinsey & Company!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Engineer - ML & AI at Mckinsey & Company.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Mckinsey & Company.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineer - ML & AI at Mckinsey & Company, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Data Engineer - ML & AI in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Mckinsey & Company, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Mckinsey & Company. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Mckinsey & Company
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Mckinsey & Company!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
