Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Research and deploy cutting-edge ML models to tackle real-world challenges.
- Company: Join DRW, a leading trading firm with a culture of innovation and integrity.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on autonomy and collaboration.
- Why this job: Make an impact in finance using advanced machine learning techniques.
- Qualifications: PhD or exceptional MSc in ML or related field with strong practical experience.
The predicted salary is between 60000 - 80000 £ per year.
DRW is a diversified trading firm with over three decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.
The Machine Learning Researcher will have a deep understanding of the principles behind modern ML algorithms — including recent advances such as transformer-based architectures and other state-of-the-art frameworks — and the ability to turn that knowledge into high-impact, production-ready solutions. The role involves applying advanced ML techniques to a wide range of forecasting challenges, building scalable ML pipelines, and deploying them in production, while working with high-dimensional, noisy, structured and unstructured datasets. Experience applying ML models in financial markets is desirable, but exceptional candidates with a strong ML background from tech, startups, or academia will also be considered.
Responsibilities
- Research, design, and deploy robust ML models.
- Build and maintain scalable, production-level ML pipelines.
- Extract signals from large, noisy, real-world datasets.
Qualifications
- PhD (or exceptional MSc) in ML, Computer Science, or related field.
- Deep theoretical and practical knowledge of core ML algorithms, and comfortable experimenting with model architectures, feature engineering, and hyperparameter tuning to produce high-performance and resilient models.
- Proven experience taking ML models from research to live production.
Quantitative Researcher - Machine Learning in London employer: DRW
Contact Detail:
DRW Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Researcher - Machine Learning in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with DRW employees on LinkedIn. A personal introduction can make all the difference when you're applying for that Quantitative Researcher role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those related to financial markets. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for technical interviews by brushing up on your ML algorithms and coding skills. Practice solving problems on platforms like LeetCode or HackerRank, focusing on areas relevant to the role at DRW.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining the DRW team.
We think you need these skills to ace Quantitative Researcher - Machine Learning in London
Some tips for your application 🫡
Show Off Your Skills: When you're writing your application, make sure to highlight your deep understanding of ML algorithms and any hands-on experience you've got. We want to see how you can turn theory into practice, especially in high-stakes environments like financial markets.
Tailor Your Application: Don’t just send a generic application! Take the time to tailor your CV and cover letter to reflect the specific skills and experiences that match the role. We love seeing candidates who take the initiative to connect their background with what we do at DRW.
Be Clear and Concise: Keep your application clear and to the point. We appreciate well-structured documents that get straight to the heart of your qualifications and experiences. Avoid jargon unless it’s relevant to the role; clarity is key!
Apply Through Our Website: Make sure to apply through our website for the best chance of getting noticed. It helps us keep track of applications and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at DRW
✨Know Your ML Algorithms Inside Out
Make sure you have a solid grasp of modern machine learning algorithms, especially the latest advancements like transformer-based architectures. Be prepared to discuss how these can be applied to real-world problems, particularly in financial markets.
✨Showcase Your Practical Experience
Bring examples of your past work where you've taken ML models from research to production. Highlight any challenges you faced and how you overcame them, as this will demonstrate your problem-solving skills and resilience.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions. Brush up on feature engineering, hyperparameter tuning, and building scalable ML pipelines. Practising coding problems related to these topics can also give you an edge.
✨Emphasise Your Collaborative Spirit
DRW values teamwork and open-mindedness. Be ready to discuss how you've worked with others in the past, especially in cross-functional teams. Show that you can communicate complex ideas clearly and are open to feedback.