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Quantitative Economics Jobs (NOW HIRING)

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 ...

Arrowstreet Capital LP

Quantitative Researcher

Boston, MA · On-site

$155K - $260K/yr

Degree from an undergraduate or graduate educational institution in finance, mathematics, economics, or a closely-related discipline emphasizing quantitative or financial analysis * 1-3 years of ...

Seven Research

Quantitative Researcher

New York, NY · On-site

$200K - $300K/yr

... from economics and other fields. What We're Looking For We seek ambitious and talented problem-solvers with strong quantitative backgrounds. No financial knowledge is expected. Quantitative ...

Point72

Cubist Quantitative Researcher

Chicago, IL · On-site

S., M.S. or PhD in finance, economics, mathematics, statistics, data science, computer science, or other quantitative discipline. * Programming in Python (or comparable language) and working ...

Seven Research

Quantitative Researcher

New York, NY · On-site

$200K - $300K/yr

... from economics and other fields. What We're Looking For We seek ambitious and talented problem-solvers with strong quantitative backgrounds. No financial knowledge is expected. Quantitative ...

Point72

Cubist Quantitative Researcher

Seattle, WA

S., M.S. or PhD in finance, economics, mathematics, statistics, data science, computer science, or other quantitative discipline. * Programming in Python (or comparable language) and working ...

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All JobsQuantitative Economics Jobs

Quantitative Economics information

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$31K

$90.6K

$146K

How much do quantitative economics jobs pay per year?

As of Jul 5, 2026, the average yearly pay for quantitative economics 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.

What is the difference between Quantitative Economics vs Data Analyst?

AspectQuantitative EconomicsData Analyst
Required CredentialsDegree in Economics, Mathematics, or related fields; often advanced degreesBachelor's or master's in Data Science, Statistics, or related fields
Work EnvironmentResearch-focused, policy analysis, academic or economic consulting firmsBusiness, marketing, finance departments, or tech companies
Employer & Industry UsageFinancial institutions, government agencies, research institutionsCorporations, marketing firms, tech companies
Common Search & Comparison IntentUnderstanding economic modeling, policy impact, research rolesData interpretation, reporting, business insights

While both roles involve data analysis, Quantitative Economics emphasizes economic modeling and policy research, often requiring advanced degrees. Data Analysts focus on interpreting data to inform business decisions, typically with a bachelor's or master's degree. The roles differ in work environment and industry focus but share skills in data handling and statistical analysis.

What are the key skills and qualifications needed to thrive as a Quantitative Economist, and why are they important?

To thrive as a Quantitative Economist, you need a strong background in economics, advanced mathematics, and statistical analysis, often supported by a degree in economics, mathematics, or a related field. Familiarity with statistical software such as R, Python, Stata, or MATLAB and experience with econometric modeling are typically required. Strong analytical thinking, problem-solving skills, and the ability to communicate complex findings to non-technical audiences are valuable soft skills. These competencies enable Quantitative Economists to analyze data effectively, inform policy or business decisions, and clearly convey insights to stakeholders.

What jobs make $1,000,000 a year?

In quantitative economics, high-earning roles such as hedge fund managers, investment bankers, and senior quantitative analysts can reach or exceed $1 million annually, especially with bonuses and profit sharing. These positions typically require advanced degrees, strong analytical skills, and experience in finance, trading, or data analysis environments.

What does a quantitative economist do?

A quantitative economist analyzes economic data and models to understand market trends, forecast economic conditions, and inform policy or business decisions. They use statistical tools, econometrics, and programming languages like R or Python to develop and test economic theories and models. This role often requires strong analytical skills and a background in economics, mathematics, or statistics.

What types of projects or analyses do Quantitative Economics professionals typically work on within organizations?

Quantitative Economics professionals often engage in projects that involve data-driven analysis, such as forecasting economic trends, evaluating policy impacts, and modeling financial markets. Their daily work may include developing econometric models, analyzing large datasets, and presenting insights to stakeholders across departments like finance, strategy, or policy. Collaboration with data scientists, business analysts, and other economists is common to ensure that quantitative findings are integrated into broader business or policy decisions. This role provides opportunities to work on high-impact projects and often leads to advancement into senior analytical or leadership positions.

What can you do with a Quantitative Economics degree?

A degree in Quantitative Economics prepares individuals for roles such as financial analyst, data analyst, economic researcher, or risk manager. These positions often require strong analytical skills, proficiency in statistical software, and understanding of economic models to analyze data and inform decision-making across finance, government, and consulting sectors.

What is quantitative economics?

Quantitative economics is a field that applies mathematical and statistical methods to analyze economic data and solve economic problems. Professionals in this area use models and quantitative techniques to make predictions, assess policy impacts, and understand economic relationships. It combines economics theory with tools from mathematics, statistics, and computer science to provide rigorous analysis and support data-driven decision making.

Is Quantitative Economics a good major?

Quantitative Economics is a strong major for careers in finance, data analysis, and economic research, as it develops skills in mathematical modeling, statistics, and economic theory. Graduates often find opportunities in banking, consulting, government agencies, and tech firms, especially when combined with programming skills in tools like R or Python. The major provides a solid foundation for analytical roles and can lead to high-demand, well-paying jobs.
More about Quantitative Economics jobs
What cities are hiring for Quantitative Economics jobs? Cities with the most Quantitative Economics job openings:
What states have the most Quantitative Economics jobs? States with the most job openings for Quantitative Economics jobs include:
What job categories do people searching Quantitative Economics jobs look for? The top searched job categories for Quantitative Economics jobs are:
Quantitative Economics jobs near you
Infographic showing various Quantitative Economics job openings in the United States as of June 2026, with employment types broken down into 83% Full Time, 16% Part Time, and 1% Contract. Highlights an 74% Physical, 5% Hybrid, and 21% Remote job distribution, with an average salary of $90,579 per year, or $43.5 per hour.

Quantitative Analyst

Amicis Global

Jersey City, NJ • On-site

$75 - $85/hr

Contractor

Posted 15 days ago

Job description

Title: Quantitative Analyst
Duration: 6+ Months
Location: Jersey City, NJ, 07311
Summary:
The Insider Risk team, in partnership with the Information Security Data Operations team, is working on a project to centralize IR data in the Cybersecurity Data Lakehouse (CyberDW). We are looking for a Data Scientist who can work with the developers and Data Analysts to perform analytics, develop risk and quant models around Insider Risk data. Ultimately, we want to create a human risk score for the Insider Risk program. This individual will be adept at ML, AI, and best practices around the new tools in the marketplace.
The Data Scientist / Data Modeler / Quantitative Analyst will play a critical role in advancing the Insider Risk program's detection, scoring, and decisioning capabilities. This role is responsible for designing, building, and continuously improving quantitative models, statistical methods, and analytical frameworks used to identify, assess, and prioritize insider risk across employees, contractors, vendors, and non‐human identities.
The role partners closely with Cyber, HR, Legal, Compliance, Anti‐Fraud, and Enterprise Information Protection to transform complex enterprise data into defensible risk signals, transparent scoring models, and executive‐level metrics that support investigations, governance, and regulatory scrutiny.
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 experience in data science, quantitative analysis, or risk modeling, preferably in financial services or regulated industries.
3) Strong experience building statistical or machine‐learning models (regression, classification, anomaly detection, clustering).
4) Proficiency in Python and/or R, with experience in SQL for large‐scale data analysis.
5) Hands‐on experience working with complex enterprise datasets and translating analytics into business decisions.
6) Strong communication skills with the ability to explain complex analytical concepts to non‐technical stakeholders.
7) Experience supporting Insider Risk, Fraud, AML, Cybersecurity, UEBA, or Threat Analytics programs.
8) Familiarity with identity and access data, endpoint telemetry, DLP, email, or collaboration monitoring.
9) Experience with model explainability, governance, and validation in regulated environments.
10) Knowledge of employee lifecycle risk, behavioral analytics, or human‐centric risk modeling.