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Statistical Arbitrage Jobs (NOW HIRING)

Engineers Gate

Quantitative Researcher

$150K - $200K/yr

DUTIES: Driving and leading research initiatives under the guidance of a Portfolio Manager to enhance statistical arbitrage-based quantitative models. Developing advanced statistical and ...

Quanta Search

Stat Arb Quant Researcher

Manhattan, NY · On-site

... statistical arbitrage environment * Experience working with Python, C++ is a plus * Expertise and success working with large and diverse data sets * Knowledge of/degree in topics including but not ...

Quanta Search

Stat Arb Quant Researcher

Manhattan, NY · On-site

... statistical arbitrage environment * Experience working with Python, C++ is a plus * Expertise and success working with large and diverse data sets * Knowledge of/degree in topics including but not ...

Quanta Search

HF Independent Trader

Westport, CT · On-site

S. equities quantitative trading businesses; high-frequency trading & statistical arbitrage trading. Ideal candidates should possess the following: Experienced U.S. equities quantitative traders ...

Quanta Search

HF Independent Trader

Westport, CT · On-site

S. equities quantitative trading businesses; high-frequency trading & statistical arbitrage trading. Ideal candidates should possess the following: • Experienced U.S. equities quantitative traders ...

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All JobsStatistical Arbitrage Jobs

Statistical Arbitrage information

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$68K

$90.1K

$107.5K

How much do statistical arbitrage jobs pay per year?

As of May 28, 2026, the average yearly pay for statistical arbitrage in the United States is $90,119.00, according to ZipRecruiter salary data. Most workers in this role earn between $73,500.00 and $106,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Statistical Arbitrage Analyst, and why are they important?

To thrive as a Statistical Arbitrage Analyst, you need strong quantitative analysis skills, advanced knowledge of statistics, mathematics, and programming, usually supported by a degree in a quantitative field like finance, math, or computer science. Familiarity with programming languages such as Python or R, experience with statistical modeling tools, and proficiency in trading platforms and data analysis systems are essential. Exceptional problem-solving abilities, attention to detail, and the capacity to work under pressure set top performers apart in this role. These skills enable analysts to develop, implement, and refine profitable trading strategies in fast-moving financial markets.

What are some common challenges faced by professionals working in statistical arbitrage roles?

Professionals in statistical arbitrage often encounter challenges such as adapting models to rapidly changing market conditions and ensuring that trading algorithms remain robust in the face of noisy data. Managing risk and avoiding overfitting when developing predictive strategies are also key concerns. Collaboration with technology teams is essential, as maintaining efficient data pipelines and low-latency execution systems can directly impact trading performance. Additionally, staying updated with advancements in quantitative methods and financial regulations is crucial for long-term success in the field.

What is statistical arbitrage?

Statistical arbitrage refers to a type of quantitative trading strategy that uses mathematical models and statistical methods to identify and exploit short-term mispricings or inefficiencies in the financial markets. Traders analyze historical price data, correlations, and patterns to make predictions about future price movements, often executing high-frequency trades across multiple securities. The goal is to profit from temporary price divergences that are expected to revert to their historical relationships. Statistical arbitrage is commonly used by hedge funds and proprietary trading firms, and it typically requires sophisticated technology and strong programming skills.

What is the difference between Statistical Arbitrage vs Quantitative Analyst?

AspectStatistical ArbitrageQuantitative Analyst
Required CredentialsDegree in finance, mathematics, or related field; strong programming skillsDegree in finance, mathematics, or related field; advanced analytical skills
Work EnvironmentTrading firms, hedge funds, proprietary trading desksFinancial institutions, investment banks, hedge funds
Industry UsageUsed for developing trading strategies based on statistical modelsUsed for analyzing markets, developing models, and advising on investments

While both roles require strong quantitative skills and similar educational backgrounds, Statistical Arbitrage focuses on developing and executing trading strategies based on statistical models, often in trading environments. Quantitative Analysts typically work on broader financial modeling, risk assessment, and investment analysis across various financial products. The roles overlap but differ mainly in their primary focus and application within the finance industry.

More about Statistical Arbitrage jobs
What cities are hiring for Statistical Arbitrage jobs? Cities with the most Statistical Arbitrage job openings:
What states have the most Statistical Arbitrage jobs? States with the most job openings for Statistical Arbitrage jobs include:
What job categories do people searching Statistical Arbitrage jobs look for? The top searched job categories for Statistical Arbitrage jobs are:
Statistical Arbitrage jobs near you
Infographic showing various Statistical Arbitrage job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $90,119 per year, or $43.3 per hour.
Statistical Arbitrage Research Analyst

Statistical Arbitrage Research Analyst

Jane Street

New York, NY • On-site

Full-time

Posted 22 days ago

Job description

About the Position
We are looking for a Statistical Arbitrage Research Analyst who is excited to apply rigorous math and statistical methods to analyze a variety of input datasets to create novel alpha-focused trading strategies for Jane Street. Your work has the potential to span across any and all liquid asset classes, including, but not limited to, U.S. and global equities, equity and fixed income futures, FX, and corporate bonds.
Ideally, you will have previous experience working in a buy-side or sell-side financial firm with some combination of asset price returns data, non-returns-based traditional data, and "alternative" data sets. However, if you are an economist or data scientist in a different field (such as tech) we're open to teaching you what you need to know to thrive in this role.
We are looking for someone who is eager to dig deep into the details of data sets to assess quality and consider outliers, dimensionality, feature engineering, causality, aligning dates across datasets, and more.
You'll help us stay vigilant in our efforts to find and correct errors or mistakes in code, which inevitably happen - though we expect this role to involve as much time delving into the lovely messiness and complexity of data as it will on advanced statistical modeling.
The problems we work on rarely have clean, definitive answers, and they often require insights from people across the firm with different areas of expertise. We find that we make the most progress when team members collaborate and communicate fluidly. Your success in this role will depend on your ability to balance expertise and intellectual rigor with an open mind to a variety of techniques and modes of thinking.
We don't believe in "one-size-fits-all" solutions; we are open to and excited about applying all different types of mathematical and statistical techniques, depending on what best fits a given problem. Progress takes place at different tempos on our team depending on the project, so you'll need to be comfortable embracing both large leaps and incremental steps forward.
About You
  • 2-6 years of professional experience working in a data-rich environment in quantitative research
  • Team player with a highly collaborative mindset; communicate clearly and often and enjoy discussing research ideas and results in depth
  • Open to a variety of techniques and modes of thinking
  • Humble about what you do and don't know; willing to admit mistakes
  • Enjoy learning new skills and teaching others what you know
  • Able to write code and analyze large datasets
  • Experienced with statistical and ML modeling
  • Knowledge of Python preferred, but not required
  • Background knowledge of financial markets is a plus

If you're a recruiting agency and want to partner with us, please reach out to agency-partnerships@janestreet.com.

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