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For Companies At a Glance
- Tasks: Design and deploy deep-learning models to enhance trading strategies.
- Company: Join a leading global proprietary trading firm known for innovation and technology.
- Benefits: Enjoy a dynamic work environment with opportunities for collaboration and growth.
- Why this job: Make a real impact in finance while working with cutting-edge technology and talented teams.
- Qualifications: Advanced degree in ML or related field, plus 3 years of relevant experience required.
- Other info: Positions available in both London and Dublin.
The predicted salary is between 48000 - 72000 £ per year.
Overview
Role: Machine Learning Quantitative Researcher
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 expanding their Machine Learning division and seeking exceptional researchers to join their teams in London and Dublin.
Responsibilities
- 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.
Qualifications
- 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.
Company
Venture Search
Tags
Industry, Machine Learning, NLP, United Kingdom
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Machine Learning Quantitative Researcher employer: NLP PEOPLE
Contact Detail:
NLP PEOPLE Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Quantitative Researcher
✨Tip Number 1
Network with professionals in the machine learning and quantitative finance sectors. Attend industry conferences, webinars, or local meetups to connect with potential colleagues and learn about the latest trends and technologies.
✨Tip Number 2
Showcase your projects and research on platforms like GitHub or personal blogs. This not only demonstrates your technical skills but also your ability to communicate complex ideas effectively, which is crucial for collaboration in multidisciplinary teams.
✨Tip Number 3
Stay updated on the latest advancements in machine learning frameworks such as PyTorch and TensorFlow. Familiarise yourself with their applications in financial datasets, as this knowledge will be invaluable during interviews and discussions with potential employers.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges and algorithmic problems related to machine learning. Focus on time-series analysis and predictive modelling, as these are key areas for the role of a Machine Learning Quantitative Researcher.
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 relevant experience in machine learning and quantitative research. Emphasise your programming skills in Python and/or C++, as well as your familiarity with ML frameworks like PyTorch or TensorFlow.
Craft a Strong Cover Letter: In your cover letter, express your passion for machine learning and quantitative research. Mention specific projects where you've built and deployed ML models, especially those related to financial datasets, to demonstrate your expertise.
Showcase Your Problem-Solving Skills: Provide examples of how you've applied scientific methods to derive insights from complex datasets. Highlight any collaborative projects that involved working with multidisciplinary teams, as this is crucial for the role.
Highlight Relevant Qualifications: Clearly state your advanced degree in Machine Learning, Computer Science, Statistics, or a related field. If you have equivalent professional experience, make sure to detail it, focusing on your ability to deliver measurable impact in fast-paced environments.
How to prepare for a job interview at NLP PEOPLE
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning 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 technical questions that assess your analytical problem-solving skills. Prepare to explain how you approach complex problems and the scientific methods you use to derive insights from large datasets.
✨Highlight Collaboration Experience
Since the role involves working closely with researchers, engineers, and traders, be ready to share examples of past collaborations. Discuss how you contributed to multidisciplinary teams and the impact of your contributions.
✨Prepare for Practical Assessments
You may be asked to complete a practical assessment or case study during the interview. Brush up on your coding skills in Python or C++, and be ready to demonstrate your ability to develop scalable research pipelines.
