At a Glance
- Tasks: Use Machine Learning to analyse vast datasets and develop innovative trading strategies.
- Company: Leading quantitative market-maker with a focus on AI and ML advancements.
- Benefits: Competitive salary, bonuses, work-life balance, and opportunities for professional growth.
- Other info: Dynamic environment with significant freedom to experiment and attend industry conferences.
- Why this job: Shape the future of ML in trading and work with enormous proprietary datasets.
- Qualifications: Deep ML expertise, strong programming skills, and collaborative communication.
The predicted salary is between 63000 - 77000 £ per year.
Salary: £200-250k base + £200-500k bonus
Client: One of the world's top quantitative market makers is expanding its Machine Learning and broader AI capabilities. Already a market leader, the firm has invested extensively in its data and research infrastructure in recent years and is continuing to grow its ML teams to stay at the forefront of the industry.
As a cross-asset liquidity provider, the firm executes huge volumes of trades every day and works with enormous datasets, creating an unusually rich and fast feedback environment for applying Machine Learning to real-world problems.
Role: You’ll use Machine Learning to extract signals and insights from vast datasets of market data, analysing data, building and testing models and developing new trading strategies. The work sits at the intersection of quantitative research, trading and software engineering. Depending on your interests and expertise, your time could be split between alpha generation within a trading team and broader ML research across the firm. The environment is extremely collaborative, with research and discoveries shared across teams to maximise PnL collectively. For example, insights developed while working with an FX trading team may have applications within Equities or other asset classes. Beyond direct trading applications, the firm is also exploring how the latest developments in ML and AI can improve research, engineering and operational workflows across the business. You’ll have significant freedom to experiment with model architectures, feature transformations and hyperparameters, while being expected to understand why techniques work and make pragmatic decisions about which approaches are appropriate for a particular problem.
Requirements:
- Deep Machine Learning expertise from either an applied or academic environment
- Strong programming and quantitative problem-solving skills
- Experience analysing large datasets and building/testing ML models
- Broad understanding of modern ML techniques and model architectures
- Ability to balance cutting-edge research with pragmatic, commercially useful solutions
- Collaborative nature with excellent communication skills
- Trading or financial markets experience is not required
Benefits:
- Help shape the firm's future ML direction and, to an extent, developments across the wider quantitative trading industry
- Work with enormous proprietary datasets in a rapid-feedback trading environment
- Significant freedom to research and experiment with new ML techniques
- Opportunities to attend leading academic and industry conferences
- Excellent compensation package
- Good work-life balance
Whilst we carefully review all applications, due to the high volume of applications we receive it is not possible to respond to those who have not been successful.
Machine Learning Researcher - Quantitative Trading- Leading Market-Maker / Hedge Fund in London employer: eFinancialCareers
As a leading global asset manager based in London, we pride ourselves on fostering a dynamic and inclusive work culture that values innovation and collaboration. Our employees benefit from comprehensive professional development opportunities, competitive compensation packages, and the chance to work with cutting-edge AI tools that enhance their skills and productivity. Join us to be part of a team that not only drives success but also prioritises employee well-being and growth in a vibrant city.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Researcher - Quantitative Trading- Leading Market-Maker / Hedge Fund in London
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We think you need these skills to ace Machine Learning Researcher - Quantitative Trading- Leading Market-Maker / Hedge Fund in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at eFinancialCareers, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at eFinancialCareers. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at eFinancialCareers
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
