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Backtesting Jobs (NOW HIRING)

Socket.dev

Quantitative Engineer

San Francisco, CA · On-site

Backtesting - Build and improve backtesting systems that measure historical strategy performance * Strategy Development - Build multi-agent systems that translate trading ideas into deployed ...

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IMC

Quantitative Developer - Python

Chicago, IL

Design high-fidelity simulation and backtesting infrastructure that models latency, microstructure, and real-world constraints * Define, compute, and curate features across instruments, regimes, and ...

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$42K

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How much do backtesting jobs pay per year?

As of Sep 12, 2026, the average yearly pay for backtesting in the United States is $102,439.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,500.00 and $119,000.00 per year, depending on experience, location, and employer.

What is backtesting?

Backtesting is the process of evaluating a trading strategy or investment model by applying it to historical market data. This helps traders and analysts see how the strategy would have performed in the past, which can provide insights into its potential effectiveness and risks. While backtesting can help identify strengths and weaknesses, it's important to remember that past performance is not always indicative of future results. The reliability of backtesting depends on data quality, strategy design, and how well it simulates real trading conditions.

What skills and qualifications are needed to thrive as a backtesting analyst?

To thrive as a Backtesting Analyst, you need a strong background in quantitative analysis, statistics, programming (typically in Python or R), and familiarity with financial markets, usually supported by a degree in mathematics, finance, or a related field. Proficiency with backtesting platforms (such as QuantConnect or Zipline), data analysis tools, and version control systems like Git is often required. Attention to detail, critical thinking, and strong problem-solving abilities are key soft skills that help ensure robust model evaluation and development. These skills are vital for accurately assessing trading strategies and minimizing risk in real-world financial applications.

What are common challenges faced when backtesting trading strategies, and how can they be managed?

One common challenge in backtesting trading strategies is the risk of overfitting, where a model performs exceptionally well on historical data but fails in live markets. Data quality and availability can also pose issues, as incomplete or inaccurate data may skew results. To manage these challenges, it's important to use out-of-sample testing, robust data cleaning processes, and to validate strategies on multiple datasets. Collaborating with quantitative analysts and developers can also help ensure the backtesting process is thorough and reliable.

What is the difference between Backtesting vs Quantitative Analyst?

AspectBacktestingQuantitative Analyst
Primary RoleTesting trading strategies using historical dataDeveloping and implementing quantitative models for investment decisions
Required SkillsData analysis, programming, finance knowledgeMathematics, programming, financial theory
Work EnvironmentTrading firms, hedge funds, financial institutionsAsset management firms, hedge funds, banks
CertificationsOften none required, but CFA or CQF helpfulCFA, CQF, or advanced degrees common

Backtesting focuses on evaluating trading strategies with historical data, while a Quantitative Analyst develops models to inform investment decisions. Both roles require strong analytical skills and finance knowledge but differ in scope and responsibilities.

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Backtesting jobs near you
Infographic showing various Backtesting job openings in the United States as of September 2026, with employment types broken down into 95% Full Time, 4% Part Time, and 1% Contract. Highlights an 82% Physical, 4% Hybrid, and 14% Remote job distribution, with an average salary of $102,439 per year, or $49.2 per hour.

Quantitative Specialist

New York, NY • On-site

Societe Generale
See more jobs at Societe Generaleexternal

Full-time

Re-posted 24 days ago

Job description

We are seeking a Quantitative Analyst to join SG R&D in AMER, focusing on Rates Algo strategies. The role involves the design, development, and support of algorithmic trading models across U.S. Treasuries and swaps markets.

The candidate will work closely with trading teams to maintain and enhance existing strategies, ensure robustness of backtesting frameworks, and contribute to the evolution of the algorithmic platform.

This role is critical to ensure continuity of expertise and mitigate key-man risk within the team.

Main Responsibilities

Algo Modeling & Development

  • Research, design, develop, implement, and maintain quantitative models for UST algo trading 

  • Enhance existing models and contribute to new developments 

  • Contribute to the development of new alpha signal strategies. 

  • Ensure robustness and scalability of core models  

    Backtesting Framework  

  • Maintain and improve backtesting infrastructure  

  • Ensure consistency, accuracy, and efficiency of simulations  

  • Contribute to performance analysis and strategy validation

Trading Support & Collaboration

  • Work closely with traders to formalize and implement trading ideas  

  • Provide support on model usage and behavior in production 

  • Participate in real-time analysis of strategy performance 

  • Collaborate with technology teams to implement the models into production

Knowledge & Documentation

  • Ensure proper documentation of models, methodologies, and workflows in line with MRM guidelines 

  • Contribute to knowledge transfer to mitigate concentration risk

Profile Required

Technical Skills

  • Strong quantitative and analytical skills  

  • Solid understanding of Rates products and derivatives  

  • Strong programming skills (Proficiency in Python, object-oriented languages) 

  • Experience in time series analysis and backtesting

Experience 

  • 2 years as quantitative analysis supporting algo trading 

  • Strong understanding of US Treasury market structure: on-the-run/off-the-run dynamics, auction cycle, repo, futures basis, and DV01 risk

    Education
     

  • Master's degree or PhD in Financial Engineering, Applied Mathematics, or related field

Apply