Statistical Arbitrage Research Analyst

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Not verified checked Jun 7, 2026

Salary

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401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source

Seniority

Mid From the posting source

Schedule

Shift type

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Weekend work

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Company

Equity

Offered From the posting source

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Statistical Arbitrage Research Analyst New York, New York, United States 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

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