Quantitative Researcher

Quantitative Researcher

London Full-Time No home office possible
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At a Glance

  • Tasks: Work on real-world projects alongside full-time mentors, solving complex financial problems.
  • Company: Join Jane Street, a leading firm in finance known for its innovative trading strategies.
  • Benefits: Enjoy social events, guest speakers, and hands-on learning experiences throughout the internship.
  • Why this job: Perfect for curious minds eager to explore finance and data science in a collaborative environment.
  • Qualifications: Strong programming skills in Python; experience in data science or machine learning 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
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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. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.

✨Tip Number 2

Network with current or former interns and employees at Jane Street. They can provide valuable insights into the company culture and the specific skills that are highly regarded, which can give you an edge in your application.

✨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 to showcase your abilities.

✨Tip Number 4

Prepare to discuss your problem-solving approach in detail. Think of examples where you've tackled complex issues, as this will demonstrate your logical thinking and intellectual curiosity, both of which are key traits for a Quantitative Researcher.

We think you need these skills to ace Quantitative Researcher

Statistical Analysis
Machine Learning Techniques
Data Science
Python Programming
Mathematical Modelling
Time Series Analysis
Feature Engineering
Data Visualisation
Problem-Solving Skills
Collaboration and Teamwork
Curiosity and Eagerness to Learn
Communication Skills
Research Methodology
Analytical Thinking

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 scenarios, and explain why you're excited about the opportunity to work at Jane Street.

Showcase 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 solve problems, 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 company. This shows that you're genuinely interested in learning and growing, which is exactly what they value.

✨Highlight Your Problem-Solving Skills

Prepare to discuss specific examples of how you've approached and solved complex problems in the past. Use the STAR method (Situation, Task, Action, Result) to structure your responses effectively.

✨Emphasise Collaboration

Since the role involves working closely with others, be sure to highlight your teamwork experiences. Share instances where you collaborated with diverse teams to achieve a common goal.

✨Brush Up on Technical Skills

Make sure you're comfortable with Python and any relevant data science or machine learning concepts. Be ready to discuss your programming experience and how you've applied it in real-world scenarios.

Quantitative Researcher
Jane Street
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