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Quantitative Finance Jobs (NOW HIRING)

Point72

Equity Quantitative Researcher

New York, NY · On-site

MS or PhD in physics, engineering, statistics, applied math, quantitative finance or other quantitative fields with a strong foundation in statistics * 1+ years of work experience in systematic alpha ...

Point72

Equity Quantitative Researcher

New York, NY · On-site

MS or PhD in physics, engineering, statistics, applied math, quantitative finance or other quantitative fields with a strong foundation in statistics * 1+ years of work experience in systematic alpha ...

Amicis Global

Quantitative Analyst

Jersey City, NJ · On-site

$75 - $85/hr

Required Skills: 1) Bachelor's or Master's degree in Data Science, Statistics, Applied Mathematics, Economics, Quantitative Finance, Computer Science, or a related discipline. 2) 5+ years of ...

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Quantitative Finance information

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$31K

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$146K

How much do quantitative finance jobs pay per year?

As of Jun 12, 2026, the average yearly pay for quantitative finance in the United States is $90,579.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,000.00 and $119,000.00 per year, depending on experience, location, and employer.

Do quants make a lot of money?

Quantitative finance professionals, or quants, often earn high salaries due to their specialized skills in mathematics, programming, and financial modeling. Compensation typically includes base salary, bonuses, and profit sharing, with total earnings frequently exceeding those of many other finance roles, especially at senior levels or in hedge funds and investment banks.

What is quantitative finance?

Quantitative finance is a field that uses mathematical models, statistics, and computational techniques to analyze financial markets and securities. Professionals in this area, known as 'quants,' develop algorithms and models to price assets, manage risk, and optimize investment strategies. Quantitative finance plays a critical role in investment banks, hedge funds, asset management firms, and financial technology companies. The field requires strong skills in mathematics, programming, and finance.

What is a quantitative finance career?

A quantitative finance career involves using mathematical models, statistical techniques, and programming skills to analyze financial markets and develop trading strategies, risk management tools, or investment models. Professionals in this field often work with large datasets, employ tools like Python or R, and may hold advanced degrees in mathematics, finance, or related fields.

What are the key skills and qualifications needed to thrive in Quantitative Finance, and why are they important?

To thrive in Quantitative Finance, you need strong mathematical, statistical, and analytical skills, typically supported by an advanced degree in mathematics, finance, engineering, or a related field. Proficiency with programming languages like Python, R, or C++, as well as experience with financial modeling and platforms such as MATLAB or Bloomberg Terminal, is highly valued. Exceptional problem-solving abilities, attention to detail, and effective communication are crucial soft skills for collaborating on complex financial projects. These skills enable professionals to develop and implement sophisticated models that drive informed investment decisions and risk management in high-stakes financial environments.

What jobs can you get with quantitative finance?

With a background in quantitative finance, common roles include quantitative analyst, risk manager, financial engineer, and algorithmic trader. These positions typically require strong skills in mathematics, programming, and data analysis, often using tools like Python, R, or MATLAB, and may require relevant certifications such as CFA or FRM.

What is the difference between Quantitative Finance vs Quantitative Analysis?

AspectQuantitative FinanceQuantitative Analysis
Required CredentialsDegree in Finance, Mathematics, or related fields; often CFA or FRM certificationsDegree in Mathematics, Statistics, or Finance; certifications like CFA are common
Work EnvironmentFinancial institutions, hedge funds, investment banksAsset management firms, banks, trading desks
Employer & Industry UsageFocuses on developing trading strategies, risk management, and financial modelingAnalyzes data to inform trading decisions, risk assessment, and investment strategies

Quantitative Finance and Quantitative Analysis share overlapping skills and credentials, but Quantitative Finance emphasizes developing financial models and trading strategies, while Quantitative Analysis focuses on data analysis to support investment decisions. Both roles are vital in finance but serve different primary functions.

What are some common challenges faced by professionals in quantitative finance roles, and how can they be addressed?

Professionals in quantitative finance often encounter challenges such as managing large and complex data sets, staying updated with rapidly evolving financial models, and ensuring accurate risk assessment in volatile markets. Collaboration with technology and trading teams is crucial to develop robust algorithms and implement models effectively. Continuous learning and adaptability are key, as the field demands keeping pace with new programming languages, statistical methods, and regulatory changes.

Is 30 too late to become a quant?

Quantitative finance is accessible to individuals who develop strong skills in mathematics, programming, and finance, regardless of age. Many quants transition into the field after gaining relevant experience or education, and age is less of a barrier than technical proficiency and continuous learning.
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Quantitative Finance jobs near you
Infographic showing various Quantitative Finance job openings in the United States as of June 2026, with employment types broken down into 95% Full Time, 3% Part Time, and 2% Contract. Highlights an 81% Physical, 8% Hybrid, and 11% Remote job distribution, with an average salary of $90,579 per year, or $43.5 per hour.

Senior Quantitative Researcher

BOTG LLC

Bridgewater, NJ • Hybrid

Other

This job post has expired today. Applications are no longer accepted.

Job description

Title: Sr. Quantitative Researcher

Location: Bridgewater, NJ (3 to 5 days a week in office)

Duration: 6 Months Contract

Role Overview

This role focuses on designing and implementing mixed integer nonlinear optimization models to support portfolio construction, strategic and tactical asset allocation, and risk return optimization. You ll work at the intersection of quantitative finance, optimization theory, and large scale computation to build models that incorporate realistic investment constraints, nonlinear risk measures, and discrete allocation decisions.

Required Skills

Strong background in optimization, quantitative finance, operations research, or applied mathematics.

Experience with nonlinear programming, mixed integer programming, and global optimization.

Proficiency in Python or Julia for modeling and data analysis.

Familiarity with portfolio theory, risk modeling, and financial instruments.

Ability to work with large datasets and high dimensional optimization problems.

Understanding of convexity, duality, and numerical stability in optimization.

Preferred Qualifications

Graduate degree (MS/PhD) in a quantitative field.

Experience with:

o Multi period or stochastic asset allocation

o Robust optimization or scenario based modeling

o Machine learning driven return or risk forecasting

Background in institutional investing, hedge funds, or asset management.

Key Responsibilities

Develop MINLP models for portfolio allocation, including:

o Cardinality constrained portfolios

o Transaction cost modeling (fixed + nonlinear costs)

o Nonlinear risk measures (e.g., downside risk, CVaR approximations, drawdown limits)

o Discrete investment decisions (e.g., lot sizes, minimum/maximum holdings, leverage rules)

Implement optimization models using Python (Pyomo, CVXPY, Gurobi interfaces), AMPL, GAMS, or JuMP.

Apply global and local solvers (e.g., BARON, Couenne, SCIP, Knitro, Ipopt) to solve non convex allocation problems.

Build custom heuristics, relaxations, or decomposition approaches to improve scalability for large universes.

Integrate optimization models into portfolio management systems and backtesting frameworks.

Conduct scenario analysis, stress testing, and sensitivity analysis on optimized portfolios.

Collaborate with investment teams to translate portfolio constraints and investment policies into mathematical formulations.

Document methodologies and present results to quantitative researchers, portfolio managers, and risk teams.

We look forward to working with you!

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