At a Glance
- Tasks: Lead the development of cutting-edge machine learning models for trading systems.
- Company: Global quantitative trading firm focused on engineering excellence and innovation.
- Benefits: Remote work flexibility, competitive salary, and opportunities for professional growth.
- Why this job: Shape the future of trading with advanced ML techniques and collaborate with top talent.
- Qualifications: 5+ years in machine learning, preferably in finance, with strong Python skills.
- Other info: Join a dynamic team dedicated to pushing the boundaries of automated trading.
The predicted salary is between 72000 - 108000 ÂŁ per year.
A global quantitative trading organization built around engineering excellence, scientific thinking, and fully automated trading. Our teams create everything in-house—from the research infrastructure to the algorithms that run live in markets around the world. We trade a wide mix of products, including equities, derivatives, options, commodities, rates, and crypto using both high‑frequency and mid‑frequency strategies. Our dedicated team members are located across multiple continents, and while we maintain physical offices, our workflow is currently designed for a remote environment.
About The Role
We’re hiring an experienced ML leader to guide the next generation of predictive modeling that powers our research and trading systems. In this role, you’ll own the strategy, design, and execution of our modeling framework—from architecture choices to validation standards to production governance. You’ll collaborate closely with quant researchers, data specialists, engineers, and traders to turn cutting‑edge research into reliable, high‑performance models used in live trading.
Responsibilities
- You’ll be responsible for one of the most critical layers of our research platform—the modeling engine that feeds our trading systems. Your work will influence how we research, validate, and deploy ML‑driven signals across the firm.
- Setting the long‑term vision for our model portfolio, covering everything from boosted trees to time‑series deep learning, graph‑based models, and advanced architectures for order‑book prediction.
- Designing training pipelines that enforce strict data hygiene—rolling and walk‑forward validation, target construction, and leakage‑free workflows.
- Building explainability and diagnostic tooling (SHAP, permutation tests, model dissection techniques) to understand model behavior.
- Developing ensemble strategies and regime‑aware model routing.
- Leading and mentoring a team of ML researchers, shaping best practices for experimentation and documentation.
- Partnering with engineering and trading teams to ensure smooth deployment of models into live trading.
Requirements/Core Experience
- 5+ years working with machine learning, with at least 2 years applying ML in quantitative finance/investments/trading.
- In depth knowledge of modern ML methods and architectures.
- Strong statistical foundation—comfortable with hypothesis testing, bootstrapping, time‑series quirks, and related methods.
- Experience building ML systems that operate in real‑time or near‑real‑time environments.
- Strong command of alpha evaluation (IC, rank correlations, stability, decay).
- Proficiency in Python and the scientific/ML ecosystem (NumPy, Pandas, PyTorch/TensorFlow).
- Understanding of market microstructure, order flow, order‑book dynamics, and factor behavior is a major plus.
- Experience guiding technical teams and shaping modeling direction.
Machine Learning Modeling Lead - DTG Capital Markets in London employer: Jobs via eFinancialCareers
Contact Detail:
Jobs via eFinancialCareers Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Modeling Lead - DTG Capital Markets in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. This is your chance to demonstrate what you can do beyond the written application.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice common ML scenarios and be ready to discuss your past experiences in detail.
✨Tip Number 4
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 our team.
We think you need these skills to ace Machine Learning Modeling Lead - DTG Capital Markets in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Machine Learning Modeling Lead. Highlight your experience in quantitative finance and any relevant ML projects you've worked on. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and trading. Share specific examples of your past work that demonstrate your expertise and how you can contribute to our team.
Showcase Your Technical Skills: Don’t forget to highlight your technical skills, especially in Python and ML frameworks like PyTorch or TensorFlow. We’re looking for someone who can hit the ground running, so make sure we see your proficiency right away!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Jobs via eFinancialCareers
✨Know Your ML Inside Out
Make sure you’re well-versed in modern machine learning methods and architectures. Brush up on your knowledge of boosted trees, time-series deep learning, and graph-based models. Being able to discuss these topics confidently will show that you’re the right fit for leading their predictive modelling efforts.
✨Showcase Your Real-Time Experience
Since this role involves building ML systems for live trading, be prepared to share specific examples of your experience in real-time or near-real-time environments. Discuss the challenges you faced and how you overcame them, as this will demonstrate your practical understanding of the field.
✨Emphasise Collaboration Skills
This position requires close collaboration with quant researchers, data specialists, and engineers. Highlight your teamwork experiences and how you’ve successfully partnered with different teams in the past. This will illustrate your ability to work effectively in a multi-disciplinary environment.
✨Prepare for Technical Questions
Expect to face technical questions related to statistical foundations and alpha evaluation metrics. Brush up on hypothesis testing, bootstrapping, and rank correlations. Being able to articulate these concepts clearly will help you stand out as a knowledgeable candidate.
