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- Tasks: Drive development of intraday and high-frequency trading strategies through alpha research and model implementation.
- Company: Eka Finance is a dynamic quant fund based in London, focused on innovative trading strategies.
- Benefits: Enjoy a hybrid work environment with opportunities for collaboration and professional growth.
- Why this job: Join a collaborative start-up atmosphere where you can impact all aspects of research and trading.
- Qualifications: Master’s or Ph.D. in a quantitative field with 5+ years in quantitative finance or trading.
- Other info: Ideal for quants seeking to expand their role beyond traditional team structures.
The predicted salary is between 43200 - 72000 £ per year.
Quant Fund Recruiting Experienced Statistical Arbitrage Researcher / Hybrid Location
Eka Finance London, United Kingdom
Position Overview: We are seeking an experienced Quantitative Researcher to drive the development and optimization of intraday and high-frequency trading strategies. This pivotal role involves conducting alpha research, implementing sophisticated models, and rigorously back-testing systematic strategies. A strong foundation in statistical arbitrage is essential, as the role demands expertise in quantitative research, data-driven decision-making, and statistical methodologies. The ideal candidate has a proven track record in either high-frequency trading, market-making, or working with leading proprietary trading firms and brings a deep understanding of statistical arbitrage principles. The candidate can come from either a medium-frequency or a high-frequency background.
Key Responsibilities
- Alpha Research & Strategy Design : Partner with the research team to uncover trading opportunities, leveraging expertise in statistical arbitrage and quantitative research. Build, refine, and implement intraday and systematic trading strategies for global markets.
- Advanced Data Analysis : Analyze large-scale market data and time-series datasets, utilizing cutting-edge statistical methods to uncover actionable patterns and insights.
- Model Development & Validation : Develop, test, and continuously enhance predictive models and systematic strategies through rigorous back-testing, ensuring their robustness across diverse asset classes (e.g., equities, currencies, commodities, and fixed income).
- Collaboration Across Teams : Coordinate closely with technology teams to integrate quantitative models with advanced trading infrastructure.
- Risk Assessment & Strategy Optimization : Apply in-depth knowledge of risk management principles to ensure that trading strategies operate within predefined risk parameters.
Key Qualifications
- Educational Background : Master’s or Ph.D. in Applied Mathematics, Statistics, Computer Science, Physics, or a related quantitative field.
- Professional Expertise :
- Demonstrated experience with statistical arbitrage strategies, high-frequency trading, or market-making.
- 5+ years of experience in quantitative finance, systematic trading, or proprietary trading environments.
- Proven ability to generate alpha through rigorous financial modeling, statistical analysis, and innovative research methods.
- Technical Proficiency :
- Strong expertise in Python (particularly for data analysis) and proficiency in C++.
- Familiarity with machine learning techniques and frameworks.
- Advanced skills in analyzing complex datasets, implementing models, and applying statistical methods to trading environments.
- Exceptional problem-solving abilities, particularly in managing complex datasets and implementing innovative solutions.
- A self-starter with a demonstrated ability to work independently in a fast-paced, high-pressure environment.
This role is open to candidates who have medium-frequency experience within statistical arbitrage. High-frequency experience is not fundamental.
If you are a quant researcher who is interested in working in a collaborative start-up environment with very good backing/peers and you want to be involved in all parts of the research/trading/coding – this is a perfect platform. Quants who are part of a larger team where they cannot get involved in all parts of the process and are just one of so many would find this ideal as a stepping stone into their career.
If you’re a driven quantitative professional with deep expertise in statistical arbitrage and a passion for cutting-edge trading strategies, we encourage you to apply. Please send a PDF CV to
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Quant Fund Recruiting Experienced Statistical Arbitrage Researcher / Hybrid Location employer: Eka Finance
Contact Detail:
Eka Finance Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quant Fund Recruiting Experienced Statistical Arbitrage Researcher / Hybrid Location
✨Tip Number 1
Make sure to showcase your experience in statistical arbitrage and quantitative research during the interview. Be prepared to discuss specific strategies you've developed or optimized, as well as the outcomes of those strategies.
✨Tip Number 2
Familiarize yourself with the latest trends and technologies in high-frequency trading and statistical methodologies. Being able to discuss recent advancements or tools can set you apart from other candidates.
✨Tip Number 3
Network with professionals in the quantitative finance space. Attend industry conferences or webinars where you can meet people from Eka Finance or similar firms, as personal connections can often lead to job opportunities.
✨Tip Number 4
Prepare to demonstrate your technical skills, especially in Python and C++. You might be asked to solve a problem or analyze data on the spot, so practice coding challenges related to data analysis and model development.
We think you need these skills to ace Quant Fund Recruiting Experienced Statistical Arbitrage Researcher / Hybrid Location
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in statistical arbitrage and quantitative research. Emphasize any relevant projects or roles that demonstrate your ability to develop and optimize trading strategies.
Craft a Strong Cover Letter: Write a cover letter that showcases your passion for quantitative finance and your specific interest in the role at Eka Finance. Mention your experience with high-frequency trading or market-making, and how it aligns with their needs.
Highlight Technical Skills: Clearly outline your technical proficiency in Python and C++, as well as any experience with machine learning techniques. Provide examples of how you've applied these skills in previous roles to analyze data and develop models.
Showcase Problem-Solving Abilities: Include examples in your application that demonstrate your exceptional problem-solving skills, particularly in managing complex datasets and implementing innovative solutions in a fast-paced environment.
How to prepare for a job interview at Eka Finance
✨Showcase Your Statistical Arbitrage Knowledge
Be prepared to discuss your understanding of statistical arbitrage principles in detail. Highlight any relevant experience you have in developing and optimizing trading strategies, and be ready to provide examples of how you've successfully applied these concepts in previous roles.
✨Demonstrate Technical Proficiency
Since the role requires strong expertise in Python and familiarity with C++, make sure to showcase your technical skills during the interview. You might be asked to solve a coding problem or explain your approach to data analysis, so brush up on your programming knowledge and be ready to discuss specific projects you've worked on.
✨Prepare for Data Analysis Questions
Expect questions that assess your ability to analyze large-scale market data and time-series datasets. Be ready to discuss the statistical methods you've used in the past, how you uncovered actionable insights, and any challenges you faced while working with complex datasets.
✨Emphasize Collaboration Skills
This role involves working closely with technology teams, so it's important to highlight your collaboration skills. Share examples of how you've successfully partnered with others in previous roles, particularly in integrating quantitative models with trading infrastructure.
