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- Tasks: Join a team applying machine learning to analyze large datasets for equity trading.
- Company: A pioneering hedge fund focused on quantitative research and collaboration.
- Benefits: Opportunity to work in a dynamic environment with a focus on academic principles.
- Why this job: Perfect for those passionate about machine learning and quantitative trading.
- Qualifications: PhD in Mathematics, Physics, or Computer Science; finance experience is a plus but not required.
- Other info: Ideal for candidates with 0-2 years of experience looking to break into the field.
The predicted salary is between 36000 - 60000 £ per year.
A pioneering and quantitatively driven hedge fund is hiring a Junior Quantitative Researcher for their Mid-Frequency Equity Statistical Arbitrage team. As part of their expansion, they are applying sophisticated machine learning techniques to analyze a variety of large datasets and enhance predictive models.
The business, a relatively unknown UK entity, values collaboration and takes an academic approach to research. Anyone joining the business will share similar principles.
The right candidate will have a stellar academic background, having studied Mathematics, Physics, or Computer Science at the PhD level from a top university. While finance experience is preferred, it is not a prerequisite if machine learning techniques (such as deep learning) have been applied to other real-world applications. Experience between 0-2 years is ideal.
If you fit the criteria and are looking to get into quantitative trading, we would love to hear from you.
Machine Learning, Quant Researcher (Equities) employer: AI Search
Contact Detail:
AI Search Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning, Quant Researcher (Equities)
✨Tip Number 1
Make sure to showcase any projects or research where you've applied machine learning techniques, especially in real-world scenarios. This will demonstrate your practical experience and understanding of the field.
✨Tip Number 2
Engage with the quantitative finance community online. Participate in forums or discussions related to statistical arbitrage and machine learning. This can help you network and learn more about the industry.
✨Tip Number 3
Familiarize yourself with the latest trends and tools in machine learning and quantitative research. Being knowledgeable about current technologies can set you apart during interviews.
✨Tip Number 4
Prepare to discuss your academic background in detail, especially how it relates to the role. Highlight any relevant coursework or research that aligns with the job description.
We think you need these skills to ace Machine Learning, Quant Researcher (Equities)
Some tips for your application 🫡
Highlight Your Academic Background: Make sure to emphasize your PhD in Mathematics, Physics, or Computer Science. Detail any relevant coursework or research that showcases your quantitative skills and understanding of machine learning techniques.
Showcase Machine Learning Experience: If you have applied machine learning techniques, especially deep learning, in real-world applications, be sure to include specific examples. Discuss the datasets you worked with and the outcomes of your projects.
Express Interest in Quantitative Trading: Clearly convey your enthusiasm for quantitative trading and how it aligns with your career goals. Mention any relevant experiences or projects that sparked your interest in this field.
Tailor Your Application: Customize your CV and cover letter to reflect the values of the company, such as collaboration and an academic approach to research. Use language that resonates with their mission and culture.
How to prepare for a job interview at AI Search
✨Showcase Your Academic Background
Be prepared to discuss your academic achievements in Mathematics, Physics, or Computer Science. Highlight any relevant projects or research that demonstrate your understanding of machine learning techniques.
✨Demonstrate Machine Learning Knowledge
Since the role emphasizes machine learning, be ready to explain your experience with various techniques, especially deep learning. Discuss specific applications where you've successfully implemented these methods.
✨Emphasize Collaboration Skills
Given the company's focus on collaboration, share examples of how you've worked effectively in teams. Highlight any experiences where you contributed to group research or projects.
✨Prepare for Technical Questions
Expect technical questions related to quantitative research and statistical arbitrage. Brush up on key concepts and be ready to solve problems or analyze datasets during the interview.
