At a Glance
- Tasks: Work on real-world projects alongside full-time mentors, solving complex problems in finance.
- Company: Join Jane Street, a leading trading firm known for its innovative approach and collaborative culture.
- Benefits: Enjoy social events, guest speakers, and hands-on learning experiences throughout the internship.
- Why this job: Dive into exciting challenges, learn from experts, and explore a career in finance without prior experience.
- Qualifications: Curiosity, programming skills in Python, and a passion for problem-solving are key; research experience is a plus.
- Other info: Open to undergraduates, graduates, and those considering a career change into finance.
Our goal is to give you a real sense of what it’s like to work at Jane Street full time. As an intern, you are paired with full-time employees who act as mentors, collaborating with you on real-world projects we actually need done. Over the course of the summer, you will explore ways to approach and solve exciting problems through fun and challenging classes, interactive sessions, and group discussions — and then you will have the chance to put those lessons to practical use. At the end of the workday, we often host guest speakers and offer a variety of social events to take part in. If you’ve never thought about a career in finance, you’re in good company. Many of us were in the same position before working here. If you have a curious mind, a collaborative spirit, and a passion for solving interesting problems, we have a feeling you’ll fit right in.
About the Position: As a Quantitative Research intern, you’ll work side by side with full-timers to learn how we identify market signals, analyse large datasets, build and test models, and create new trading strategies. At Jane Street, we blur the lines between trading and research, fostering a fluid environment where teams work in a tight loop to solve complex problems. We don’t believe in “one-size-fits-all” modelling solutions; we are open to and excited about applying all different types of statistical and ML techniques, from linear models to deep learning, depending on what best fits a given problem.
Our advanced proprietary trading models are the backbone of our operation, enabling us to identify profitable trading opportunities across hundreds of thousands of financial products, in over 200 trading venues globally. We utilise petabytes of data, our computing cluster with hundreds of thousands of cores, and our growing GPU cluster containing thousands of A/H100s to develop trading strategies in adversarial markets that evolve every day.
During the programme you’ll focus on two projects, mentored closely by the key stakeholders who’ve worked on them. You may conduct a study of some new or existing dataset, build new tools that support the firm’s research, or consider big-picture questions that we’re still trying to figure out. The problems we work on rarely have clean, definitive answers — and they often require insights from colleagues across the firm with different areas of expertise. You’ll gain a better understanding of the diverse array of research challenges we consider every day, learning how we think about dataset generation, time series analysis, feature engineering, and model building for financial datasets.
Your day-to-day project work will be complemented by classes on the broader fundamentals of markets and trading, lunch seminars, and activities designed to help you understand the entire process of creating a new trading strategy, from initial exploration to finding and productionising a signal.
Most interns are current undergraduate or graduate students, but we also welcome applicants who have already graduated and are considering a new career in finance. We don’t expect you to have a background in finance or any other specific field — we’re looking for smart, ambitious people who enjoy solving challenging problems. Most candidates will have experience with data science or machine learning, but ultimately, we’re more interested in how you think and learn than what you currently know.
You should be:
- Able to apply logical and mathematical thinking to all kinds of problems
- Intellectually curious; eager to ask questions, admit mistakes, and learn new things
- A strong programmer who’s comfortable with Python
- An open-minded thinker and precise communicator who enjoys collaborating with colleagues from a wide range of backgrounds and areas of expertise
- Research experience a plus
Quantitative Researcher employer: Jane Street
Contact Detail:
Jane Street Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Researcher
✨Tip Number 1
Familiarise yourself with the latest trends in quantitative research and trading strategies. Read up on statistical methods, machine learning techniques, and how they apply to finance. This knowledge will not only help you during interviews but also show your genuine interest in the field.
✨Tip Number 2
Network with current or former interns and employees at Jane Street. Use platforms like LinkedIn to connect and ask about their experiences. This can provide you with valuable insights into the company culture and the skills that are most valued.
✨Tip Number 3
Brush up on your programming skills, especially in Python. Consider working on personal projects or contributing to open-source projects that involve data analysis or machine learning. This practical experience can set you apart from other candidates.
✨Tip Number 4
Prepare for problem-solving discussions by practising with real-world datasets. Try to analyse them, build models, and derive insights. Being able to demonstrate your thought process and approach to complex problems will be crucial during your interactions with the team.
We think you need these skills to ace Quantitative Researcher
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and expectations of a Quantitative Research intern at Jane Street. Familiarise yourself with the skills required, such as programming in Python and experience with data science or machine learning.
Tailor Your CV: Craft your CV to highlight relevant experiences and skills that align with the job description. Emphasise any projects or coursework related to data analysis, statistical modelling, or programming, showcasing your logical and mathematical thinking.
Write a Compelling Cover Letter: In your cover letter, express your intellectual curiosity and passion for solving complex problems. Mention specific examples of how you've applied your skills in real-world situations, and explain why you're excited about the opportunity to work at Jane Street.
Showcase Your Collaborative Spirit: Since the role involves working closely with others, highlight any teamwork experiences in your application. Discuss how you've collaborated with diverse groups to achieve common goals, demonstrating your open-mindedness and communication skills.
How to prepare for a job interview at Jane Street
✨Show Your Curiosity
Demonstrate your intellectual curiosity by asking insightful questions about the role and the projects you'll be working on. This shows that you're eager to learn and engage with the work, which is highly valued at Jane Street.
✨Highlight Your Problem-Solving Skills
Prepare examples of how you've approached complex problems in the past, especially using data science or machine learning techniques. Be ready to discuss your thought process and the outcomes of your solutions.
✨Emphasise Collaboration
Since the role involves working closely with others, share experiences where you've successfully collaborated with diverse teams. Highlight your communication skills and how you adapt to different perspectives.
✨Demonstrate Programming Proficiency
Be prepared to discuss your programming experience, particularly with Python. You might be asked to solve a coding problem or explain your approach to a technical challenge, so brush up on your coding skills beforehand.