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- Tasks: Design and deploy deep-learning models to enhance trading strategies and forecast market behaviour.
- Company: Join a leading global proprietary trading firm known for innovation and advanced technology.
- Benefits: Enjoy a full-time role with opportunities for collaboration and impactful work in finance.
- Why this job: Be part of a dynamic team solving complex problems in fast-paced financial environments.
- Qualifications: Advanced degree or equivalent experience in Machine Learning, with strong programming skills in Python or C++.
- Other info: Work in London or Dublin and collaborate with multidisciplinary teams.
The predicted salary is between 43200 - 72000 ÂŁ per year.
Direct message the job poster from Venture Search
Senior Consultant at Venture Search – Hedge Funds & Proprietary Trading
Location: London / Dublin
Venture Search has partnered with a leading global proprietary trading firm known for its decades of experience, advanced use of technology, quantitative research, and market innovation.
Our partner is now expanding their Machine Learning division and seeking exceptional researchers to join their teams in London and Dublin.
The Role:
- Design, build, and deploy deep-learning-based models to forecast market behaviour and enhance trading strategies.
- Collaborate closely with researchers, engineers, and traders to refine models and explore innovative algorithmic solutions.
- Develop scalable end-to-end research pipelines and conduct experiments using modern ML frameworks.
- Apply scientific methods to translate large, complex datasets into actionable trading signals.
- Work with engineering teams to integrate your research into live production systems.
What We’re Looking For:
- An advanced degree in Machine Learning, Computer Science, Statistics, or a related quantitative discipline—or equivalent research-driven professional experience.
- At least 3 years of experience building and deploying ML models, especially for time-series financial datasets.
- Demonstrated ability to derive predictive insights from large-scale data.
- Strong programming expertise in Python and/or C++, with hands-on experience using ML frameworks like PyTorch or TensorFlow in production settings.
- Deep learning experience applied to forecasting, signal generation, and optimization.
- Robust foundations in mathematics, statistics, and algorithmic thinking, coupled with exceptional analytical problem-solving skills.
- A collaborative mindset with the ability to contribute meaningfully in multidisciplinary research-engineering-trading settings.
- A passion for solving complex problems and delivering measurable impact in fast-paced financial environments.
Seniority level
-
Seniority level
Mid-Senior level
Employment type
-
Employment type
Full-time
Job function
-
Job function
Finance
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Machine Learning Quantitative Researcher employer: Venture Search
Contact Detail:
Venture Search Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Quantitative Researcher
✨Tip Number 1
Make sure to showcase your experience with deep learning models in your conversations. When you connect with the job poster or during networking events, highlight specific projects where you've successfully deployed ML models, especially in financial contexts.
✨Tip Number 2
Familiarise yourself with the latest trends in quantitative trading and machine learning. Being able to discuss recent advancements or case studies can demonstrate your passion and knowledge, making you a more attractive candidate.
✨Tip Number 3
Engage with professionals in the field through platforms like LinkedIn. Join relevant groups or forums where you can share insights and ask questions about machine learning applications in trading, which can help you build connections and gain visibility.
✨Tip Number 4
Prepare to discuss your collaborative experiences. Since the role involves working closely with engineers and traders, think of examples where you've successfully collaborated in multidisciplinary teams to solve complex problems.
We think you need these skills to ace Machine Learning Quantitative Researcher
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in machine learning, particularly with time-series financial datasets. Emphasise your programming skills in Python and/or C++, as well as your familiarity with ML frameworks like PyTorch or TensorFlow.
Craft a Compelling Cover Letter: In your cover letter, express your passion for solving complex problems in fast-paced financial environments. Mention specific projects where you've successfully built and deployed ML models, and how they contributed to trading strategies.
Showcase Your Research Experience: Detail any relevant research experience you have, especially if it involves translating large datasets into actionable insights. Highlight your ability to collaborate with multidisciplinary teams, as this is crucial for the role.
Prepare for Technical Questions: Be ready to discuss your technical expertise in depth. Prepare examples of how you've applied scientific methods in your work, and be prepared to explain your thought process when developing algorithms or models.
How to prepare for a job interview at Venture Search
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, C++, and ML frameworks like PyTorch or TensorFlow. Bring examples of projects where you've built and deployed models, especially those related to time-series financial datasets.
✨Demonstrate Problem-Solving Abilities
Expect to face complex problem-solving scenarios during the interview. Practice articulating your thought process when tackling analytical challenges, particularly in a financial context.
✨Highlight Collaborative Experiences
Since the role involves working closely with researchers, engineers, and traders, share specific instances where you successfully collaborated in multidisciplinary teams. Emphasise your ability to communicate technical concepts to non-technical stakeholders.
✨Prepare for Deep Learning Discussions
Brush up on your knowledge of deep learning techniques and their applications in forecasting and signal generation. Be ready to discuss how you've applied these methods in previous roles and the impact they had on trading strategies.
