At a Glance
- Tasks: Develop advanced models to optimise GPU usage and improve infrastructure efficiency.
- Company: CoreWeave, a leading cloud provider for AI innovation.
- Benefits: Comprehensive medical and dental insurance, generous pension, and tuition reimbursement.
- Other info: Join a hybrid work culture focused on innovative disruption and career growth.
- Why this job: Make a real impact in AI by turning complex data into actionable insights.
- Qualifications: MS or PhD in a quantitative field with strong Python skills and experience in machine learning.
The predicted salary is between 70000 - 90000 £ per year.
CoreWeave is the essential cloud for AI, delivering technology, tools, and teams that enable innovators to build and scale AI with confidence. Founded in 2017 and publicly traded (Nasdaq: CRWV) in March 2025, CoreWeave is trusted by leading AI labs, startups, and global enterprises.
The Monolith Data Science team is building a layered reliability platform that shifts CoreWeave from reactive troubleshooting to proactive reliability engineering. The platform spans telemetry ingestion, feature engineering, anomaly detection, failure prediction, distributed straggler detection, and agentic root cause analysis. You will partner closely with Fleet, Infrastructure, and AI Platform teams to improve cluster reliability, increase effective utilization (MFU), reduce MTTR, and protect uptime and revenue.
As a Data Science Researcher, you will develop advanced statistical models and machine learning methodologies to optimize GPU utilization, workload scheduling, and infrastructure efficiency. You will design experiments, analyze large‑scale system telemetry data, and prototype predictive and optimization algorithms that directly inform production systems. This role blends research rigor with real‑world impact, turning complex infrastructure data into measurable improvements in performance and cost, and you will collaborate cross‑functionally to translate research insights into deployable solutions.
Who You Are
- MS or PhD in Computer Science, Statistics, Applied Mathematics, Machine Learning, or related quantitative field
- 8+ years (or equivalent research experience) applying statistical modeling or machine learning to large‑scale datasets
- Strong proficiency in Python and scientific computing libraries (NumPy, pandas, SciPy, scikit‑learn, PyTorch or TensorFlow)
- Demonstrated experience designing and analyzing controlled experiments (A/B testing, causal inference, hypothesis testing)
- Experience working with distributed data systems (Spark, Ray, Dask, or similar)
- Proficiency in SQL and working with large‑scale structured datasets
- Experience building and validating predictive models in production or research environments
- Strong understanding of optimization techniques (linear programming, convex optimization, stochastic optimization, or reinforcement learning)
- Experience with time‑series data and performance telemetry
- Ability to translate research findings into production‑ready prototypes
Preferred
- PhD with published research in systems optimization, distributed computing, ML systems, or performance modeling
- Experience with GPU workloads, distributed training, or AI infrastructure
- Familiarity with Kubernetes, containerized workloads, or cloud‑native systems
- Experience developing reinforcement learning or adaptive scheduling systems
- Background in capacity planning, forecasting, or resource allocation modeling
- Contributions to open‑source ML or systems projects
What We Offer
- Family‑level Medical Insurance
- Family‑level Dental Insurance
- Generous Pension Contribution
- Life Assurance at 4× Salary
- Critical Illness Cover
- Employee Assistance Programme
- Tuition Reimbursement
- Work culture focused on innovative disruption
Workplace
While we prioritize a hybrid work environment, remote work may be considered for candidates located more than 30 miles from an office, based on role requirements for specialized skill sets. New hires will be invited to attend onboarding at one of our hubs within their first month.
EEO Statement
CoreWeave is an equal opportunity employer, committed to fostering an inclusive and supportive workplace. All qualified applicants and candidates will receive consideration for employment without regard to race, color, religion, sex, disability, age, sexual orientation, gender identity, national origin, veteran status, or genetic information.
Data Scientist employer: CoreWeave
CoreWeave is an exceptional employer, offering a dynamic work environment in Greater London where innovation meets reliability. With comprehensive benefits such as family-level medical and dental insurance, pension contributions, and tuition reimbursement, employees are supported both personally and professionally. The company fosters a culture of growth and collaboration, ensuring that every Data Centre Technician has the opportunity to develop their skills while contributing to cutting-edge data centre operations.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at CoreWeave. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! Prepare a portfolio or a GitHub repo showcasing your projects, especially those related to machine learning and data science. This is your chance to shine beyond the CV!
✨Tip Number 3
Ace the interview by practising common data science questions and case studies. We recommend simulating interviews with friends or using online platforms to get comfortable with the format.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining the CoreWeave team.
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Data Scientist role. Highlight your proficiency in Python, machine learning, and any relevant projects you've worked on. We want to see how you can contribute to our mission!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background aligns with our goals at CoreWeave. Be sure to mention any experience with large-scale datasets or distributed systems.
Showcase Your Projects:If you've worked on any interesting projects, especially those involving statistical models or machine learning, make sure to include them. We love seeing real-world applications of your skills, so don’t hold back!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at CoreWeave
✨Know Your Data Science Stuff
Make sure you brush up on your statistical models and machine learning methodologies. Be ready to discuss how you've applied these in real-world scenarios, especially with large-scale datasets. CoreWeave is looking for someone who can turn complex data into actionable insights, so have some examples up your sleeve!
✨Get Familiar with Their Tech Stack
CoreWeave uses Python and various scientific computing libraries like NumPy and PyTorch. If you’ve worked with distributed data systems or SQL, be prepared to talk about your experience. Showing that you understand their tech stack will definitely give you an edge.
✨Prepare for Problem-Solving Questions
Expect questions that test your ability to design experiments and analyse telemetry data. Think about how you would approach A/B testing or causal inference in a practical setting. Being able to articulate your thought process will demonstrate your analytical skills.
✨Show Your Collaborative Spirit
Since the role involves working closely with other teams, be ready to discuss how you've collaborated in the past. Share examples of cross-functional projects where you translated research findings into deployable solutions. This will highlight your teamwork skills and adaptability.
