Quant Trader Internship 2027 (6 months)
Maven
Applying takes a free account: you'll come right back to this job to finish with your profile.
Need a reasonable accommodation to apply or interview? Contact us.
JobMinglr uses automated technology to recommend jobs based on profile information and job preferences. Match Score does not determine eligibility for a position, prevent a user from viewing or applying to a job, or make hiring decisions on behalf of an employer.
Description
Maven Securities is a leading proprietary trading firm that leverages sophisticated technology and quantitative analysis to trade in global financial markets. We specialise in options and derivatives market making, where your skills can be directly applied to have a real impact.
Please note, this Quant Trader Internship is 6 months, full-time. If you would like to be considered for our Chicago Trader Summer Internship, please apply here. Alternatively, if you would like to be considered for our London Trader Summer Internship, applications will open on 7th September. We can only accept one application per candidate per recruitment cycle, so please ensure you apply to the role you are most interested in.
THE ROLE
As a Quantitative Trader Intern, you’ll engage in an intensive 6-month training programme focused on options market making. You’ll collaborate closely with traders and researchers to solve meaningful research challenges and develop your expertise in quantitative methods across our market-making desks. Upon successfully completing the internship, you will be eligible for consideration for a full time position. As a Quant Trader Intern, your responsibilities will include:
- Solving quantitative research problems such as volatility modelling, alpha generation and validation
- Creating visualisation tools using Python or AI-assisted code
- Completing a structured research project under the mentorship of traders and quants
- Shadowing our Options Market Making desks
WHAT ARE WE LOOKING FOR
- Background in Applied Mathematics, Financial Engineering, Physics, or a similar quantitative discipline
- Proficient in programming with at least one language (preferably Python however C++ or C# is advantageous too)
- Quick and creative problem-solver, able to tackle new or unfamiliar challenges independently
- Strong communicator with the ability to present research findings clearly and effectively
- Self-starter with a keen interest in refining current trading strategies and uncovering new opportunities
- Familiarity with financial markets is a plus, though not essential
- Professional-level fluency in English
WHAT WE CAN OFFER YOU:
- A fast-growing global firm with plenty of opportunities to have a significant impact
- Flat hierarchal environment and informal dress code
- Competitive compensation package
- On-site private gym with instructor-led classes including boxing, yoga, and more
- Monthly company events and social activities
- Flexible start date: January 2027 or July 2027
PLEASE NOTE
- We only accept one application per candidate per recruitment cycle
- Visa sponsorship is available for this role and will be discussed with eligible candidates at later stages of the process
- For more information on our assessment process and commonly asked questions, please visit the Emerging Talent page on Maven’s website
How this employer is doing
solid
- News: Growing or Expanding
- H1B: Sponsors Visas
- Leadership Change
- Recently Raised Funding
In the news
Pentagon appoints new Maven Smart System program director in fresh push for C2 integration, DefenseScoop
Pay for this role
The employer didn't post a pay range for this role. That usually means pay is set in negotiation, which favors whoever arrives with numbers. Check ranges on comparable Quant Trader Internship 2027 (6 months) postings in Greater London, and analyze any offer before you accept it.
Before you apply, worth reading
How JobMinglr reads this job
Every listing here is scored against your profile before you apply: skills overlap, experience level, location and work arrangement, each weighted and explained. You see the score and the reasons, not just a list. How the matching works.