Quantitative Researcher at Akuna Capital

Chicago, Illinois, United States -

Full Time

Start Date

Immediate

Expiry Date

02 Apr, 26

Salary

130000.0

Posted On

03 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Statistics, Machine Learning, Python, C++, Collaboration, Communication, Predictive Models, Trading Strategies

Industry

Capital Markets

Description

About Akuna: Akuna Capital is an innovative trading firm with a strong focus on collaboration, cutting-edge technology, data driven solutions, and automation. We specialize in providing liquidity as an options market-maker – meaning we are committed to providing competitive quotes that we are willing to both buy and sell. To do this successfully, we design and implement our own low latency technologies, trading strategies, and mathematical models. Our Founding Partners first conceptualized Akuna in their hometown of Sydney. They opened the firm’s first office in 2011 in the heart of the derivatives industry and the options capital of the world – Chicago. Today, Akuna is proud to operate from additional offices in Sydney, Shanghai, London, and Singapore. What you’ll do as a Quantitative Researcher at Akuna: Akuna’s Trading and Research teams are seeking Quant Researchers to join a multidisciplinary group of mathematicians, statisticians, technologists and traders. These teams drive the development of trading strategies and predictive models by combining quantitative rigor with deep expertise in financial markets. We are looking for talented researchers who can apply and develop machine learning algorithms to contribute to Akuna’s strategy portfolio. In this role, you will: Develop trading strategies using statistical and machine learning algorithms Drive improvements in predictive models through rigorous signal and feature research Design and optimize machine learning workflows to support scalable, efficient, and reproducible research Bring a results-driven mindset paired with strong collaboration and communication skills. Qualities that make great candidates: 3+ years of strong professional work experience in statistics, machine learning or related area BS/MS/PhD degree in a technical field – Engineering, Computer Science, Math, Physics, or similar Proven research background in academic or professional environment Programming experience in Python or C++ Expertise in statistics and machine learning Financial experience or knowledge of trading is a plus The ability to react quickly and accurately to rapidly changing market conditions, including the ability to quickly and accurately respond and/or solve math and coding problems are essential functions of the role In addition to technical skillsets, Akuna values the unique perspectives people can bring to the table to collaboratively solve complex problems and drive Akuna forward. We want all groups to feel empowered to apply, even if you don’t meet every qualification. We welcome your application and encourage you to take the first steps toward your future with us! Please note that level/title, as well as team placement, will be determined upon the conclusion of the interview process. If you are a current student or recent graduate, please take a look at our Entry Level and Intern positions. In accordance with Illinois Equal Pay Act, the minimum base salary starts at $130,000. Exact compensation offered may vary based on many factors including, but not limited to, the candidate’s experience, qualifications, and skill set. This role is also eligible for a discretionary performance bonus as part of the total compensation package and includes a comprehensive benefits package that may encompass employer-paid medical, dental, vision, retirement contributions, paid time off, and other benefits. The minimum base salary herein was determined in good faith by Akuna Capital LLC.

Responsibilities

As a Quantitative Researcher, you will develop trading strategies using statistical and machine learning algorithms and drive improvements in predictive models. You will also design and optimize machine learning workflows to support scalable and efficient research.