Data Engineer - Scientific AI
Location: San Francisco, California, US
Recruiter: McKinsey & Company
Date posted: Thursday, November 6, 2025
Job Description:
Driving 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.
In return for your drive, determination, and curiosity, we'll provide the resources, mentorship, and opportunities you need to become a stronger leader faster than you ever thought possible. Your colleaguesat all levelswill invest deeply in your development, just as much as they invest in delivering exceptional results for clients. Every day, you'll receive apprenticeship, coaching, and exposure that will accelerate your growth in ways you wont find anywhere else.
When you join us, you will have:
- 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. Youll make a tangible impact by offering innovative ideas and practical solutions. 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 firms diversity fuels creativity and helps us come up with the best solutions for our clients. Plus, youll 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.
You will work with cutting edge AI teams on research and development topics across our life sciences, global energy and materials (GEM), and advanced industries (AI) practices, serving as a data engineer/machine learning engineer in a technology development and delivery capacity.
Youll be working in one of our offices in North America in our Life Sciences practice. You will work as a member of McKinseys global Scientific AI team helping to answer industry questions related to how AI can be used for therapeutics, chemicals & materials (including small molecules, proteins, mRNA, polymers, etc.).
With your expertise in computer science, computer engineering, cloud, and data transformation (ETL & feature engineering), you will help build and shape McKinseys scientific AI offering.
You will deliver distinctive capabilities, data, and machine learning systems through your work with client teams and clients. You will play a pivotal role in the creation/dissemination of cutting-edge knowledge and proprietary assets, and you will help build the firms reputation in your area of expertise.
As a Data Engineer, your role will be split between developing new internal knowledge, building AI and machine learning models & pipelines, supporting client discussions, prototypes development, and deploying directly with client delivery teams.
Your core responsibilities will entail:
- Bringing distinctive data/machine learning engineering & product development competency to complex client problems through part-time staffing on clients
- Supporting the manager of data engineering/machine learning engineering on the development of data/machine learning engineering roadmap of assets across cell-level initiatives
- Productionize AI prototypes/create deployment ready solutions
- Translating engineering concepts and design/architecture trade-offs and decisions for senior stakeholders
- Writing optimized code to advance our AI Toolbox and codify methodologies for future deployment
- Working in a multi-disciplinary team
- Masters degree with 2+ years or PhD degree with 1+ years of relevant experience in computer science, computer engineering or equivalent experience with experience in research
- ETL, big data experience and tooling (i.e., PySpark, Databricks), Python testing frameworks, data validation and data quality frameworks, data handing (SQL & NoSQL), feature engineering, chunking, document ingestion, graph data structures (i.e., Neo4j), CI/CD pipelines, basic K8s (manifests, debugging, docker, Argo Workflows), MLflow deployment and usage, GenAI frameworks (LangChain), GPU model development / deployment
- Proven experience applying machine learning techniques to solve business problems
- Experience in client delivery with direct client contact
- Proven experience in translating technical methods to non-technical stakeholders
- Strong programming experience in python (Python, Java, C++, SQL) and experience with cloud development platforms such as AWS, Azure, Google (and appropriate Bash/Shell scripting)
- Experience with version control (GitHub)
- Ability to write production code and object-oriented programming is a plus
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