Quantitative Researcher | Trading Team
Jump Trading · On-site
MeritLog read this listing from Jump Trading's Greenhouse job board and last checked it on September 12, 2026.
Source: the employer's Greenhouse job board. Open the original listing for current details.
Job details
- Work model
- On-site
- Salary
- Not listed by source
- Location
- London
- Occupation
- Data Scientists(O*NET 15-2051.00)
What the role asks for
What they're asking for
- Proven success with profitable trading strategiesSkill
- Strong programming skills in C++/Python in a Linux environmentSkill
- Working knowledge of forecasting and data mining techniques, such as linear and non-linear regression analysis, neural networks, or support vector machinesSkill
- Strong experience developing statistical models in a trading environmentSkill
- Proven success working with large data sets and developing statistical modelsSkill
- Fascinated and interested in advancing machine learning within the trading communitySkill
- Possess strong familiarity with Python, R or MATLAB along with development skills to support research effortsSkill
- Masters or PhD in Statistics, Physics, Mathematics (or related subject)Education
- Desire to work within a collaborative, team-driven environmentSkill
- Reliable and predictable availabilitySkill
Parsed by MeritLog from the employer’s own posting. The full description follows below.
Job description
Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. The quantitative trading teams at Jump Trading probe and examine the global markets, seeking to understand the complexities of various traded products and exchanges. They leverage their impeccable statistical analysis and data mining skills, using the results of their research to make forecasts and develop profitable predictive trading models. What You'll Do: Quantitative Researchers collect and analyze tens of thousands of data sets, identify patterns and extract insights into the complexities in financial markets. Researchers lean heavily on statistical analysis, machine learning, and data engineering skills; applying the results of their research to forecasts and predictive trading models. Jump’s Quantitative Researchers are constantly collaborating with other scientists, traders, hardware and software developers, and market facing business teams to push for the best expression of our new ideas. Other duties as assigned or needed. Skills You’ll Need: • Proven success with profitable trading strategies • Strong programming skills in C++/Python in a Linux environment • Working knowledge of forecasting and data mining techniques, such as linear and non-linear regression analysis, neural networks, or support vector machines • Strong experience developing statistical models in a trading environment • Proven success working with large data sets and developing statistical models • Fascinated and interested in advancing machine learning within the trading community • Possess strong familiarity with Python, R or MATLAB along with development skills to support research efforts • Masters or PhD in Statistics, Physics, Mathematics (or related subject) • Desire to work within a collaborative, team-driven environment • Reliable and predictable availability Benefits include: • Private Medical, Vision and Dental Insurance • Travel Medical Insurance • Group Pension Scheme • Group Life Assurance and Income Protection Schemes • Paid Parental Leave • Parking and Cycle Schemes
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