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Remote Quantitative Analyst Jobs in Springfield, VA

Addison Group

Senior Financial Analyst

Tysons, VA · Remote

$100K - $110K/yr

Remote Schedule: Monday-Friday | EST business hours Compensation: $100,000 - $110,000 / Annually ... Strong quantitative, analytical, and critical thinking skills * Ability to manage multiple ...

BlackSky

Senior Business Intelligence Analyst

Herndon, VA · On-site +1

Senior Analyst, Business Intelligence About Us: BlackSky is a real-time intelligence company. We ... quantitative or technical field, or an MBA. * Professional experience with geospatial and/or remote ...

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Aretum

CX Research Analyst

Mclean, VA · Remote

$105K - $120K/yr

Conduct stakeholder interviews, surveys, user research, and other qualitative and quantitative ... Travel Requirements This is a remote position; however, occasional travel may be required based on ...

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Remote Quantitative Analyst information

See Springfield, VA salary details

$59K

$139.8K

$250.7K

How much do remote quantitative analyst jobs pay per year?

As of Sep 14, 2026, the average yearly pay for remote quantitative analyst in Springfield, VA is $139,838.00, according to ZipRecruiter salary data. Most workers in this role earn between $116,500.00 and $152,000.00 per year, depending on experience, location, and employer.

What is a remote quantitative analyst?

A Remote Quantitative Analyst is a professional who applies mathematical, statistical, and computational techniques to analyze financial data, build models, and support decision-making—while working from a remote location. They often work in industries like finance, investment banking, hedge funds, and fintech, focusing on risk management, trading strategies, and portfolio optimization. This role requires expertise in programming (Python, R, SQL), data analysis, and financial modeling. Remote Quantitative Analysts collaborate with teams via digital tools and must be self-motivated and detail-oriented.

What skills and qualifications are needed to be a remote quantitative analyst?

To thrive as a Remote Quantitative Analyst, you need strong analytical and mathematical skills, a degree in a quantitative field like mathematics, statistics, or finance, and experience applying quantitative methods to real-world problems. Proficiency in statistical programming languages such as Python, R, or MATLAB, as well as expertise in data visualization tools and familiarity with databases, are highly valued, and certifications like CFA or FRM may be advantageous. Excellent communication, self-motivation, time management, and collaboration skills are essential for remote work, enabling you to explain complex findings clearly and work effectively across virtual teams. These abilities ensure accurate analysis, effective collaboration, and the delivery of actionable insights in a distributed work environment.

What challenges do remote quantitative analysts face and how can they be addressed?

Remote Quantitative Analysts often face challenges such as managing complex data sets independently, ensuring clear communication with team members across different locations, and maintaining focus without direct supervision. To overcome these hurdles, it's important to establish a structured daily routine, utilize collaboration tools like Slack or Zoom for real-time communication, and document analyses thoroughly for transparency. Regular check-ins with colleagues and participation in virtual brainstorming sessions can also foster a sense of teamwork and facilitate problem-solving. Proactively seeking feedback and staying updated on industry best practices will help you grow and excel in this dynamic remote role.

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Remote Quantitative Analyst jobs near you

Lead Analyst - Quantitative Social Science Research

Washington, DC • Remote

BETA Systems International
IT Services • 1 - 10 employees
Learn more about BETA Systems Internationalexternal

Contractor

Posted 8 days ago

Job description

Location: Remote
Employment Type: Contract / Project-Based
Anticipated Period: October 2026 – September 2027
Company: The Brandon Green Management Group (BGMG)
Project: Federal Correctional Officer Retention AnalysisPosition Summary

BGMG is seeking is seeking a Lead Analyst to lead quantitative analysis for a federal workforce research project examining correctional officer attrition and monetary retention incentives.
**This role is pending subject to contract award**

The Lead Analyst will lead statistical analysis using R, including regression analysis and quasi-experimental designs (QEDs). The work is expected to include discrete-time survival analysis and staggered difference-in-differences analysis. The analyst must be able to assess model assumptions, adjust analytic methods when needed, document code, and explain complex findings in plain language.

Key Responsibilities
  • Serve as technical lead for quantitative research and statistical analysis.
  • Refine and expand government-provided analysis plans.
  • Conduct descriptive analysis of correctional officer attrition using workforce and payroll data covering 2001–2026.
  • Examine how attrition risk changes over an employee's career.
  • Analyze individual and institution-level factors associated with attrition.
  • Conduct discrete-time survival analysis, including logistic regression where appropriate.
  • Analyze relationships between monetary retention incentives and attrition.
  • Conduct staggered difference-in-differences or other appropriate quasi-experimental analyses.
  • Examine dose-response relationships between incentive levels and attrition.
  • Assess interactions involving institution characteristics such as rurality and security level.
  • Evaluate model assumptions and adjust statistical methods when supported by the data.
  • Conduct sensitivity, diagnostic, and robustness analyses as appropriate.
  • Write clean, documented, reproducible R code.
  • Prepare shareable R code that allows DOJ to replicate analyses.
  • Interpret statistical results and limitations.
  • Draft technical findings for interim and final reports.
  • Translate complex quantitative results into concise, plain-language findings for decision makers.
  • Support development of presentation materials for non-technical audiences.
  • Provide technical leadership to supporting researchers and analysts.

The solicitation places specific emphasis on deep quantitative expertise and the ability to adjust analytic techniques based on the available data rather than simply executing a predetermined model.

Minimum Qualifications

Candidates must meet one of the following education paths:

  • Doctor of Philosophy (Ph.D.) in statistics, economics, or another quantitative social science from an accredited institution; or
  • Master’s degree in statistics, economics, or another quantitative social science plus three additional years of related professional experience above the minimum requirement.

Candidates must also have:

  • At least 5 years of professional experience in applied quantitative social science research.
  • Expertise writing R code.
  • Experience implementing regression analyses in the context of quasi-experimental designs.
  • Experience serving as a team leader.
  • Experience translating complex quantitative findings into plain-language, concise findings.
Preferred Qualifications
  • Workforce or labor research experience.
  • Difference-in-differences analysis experience.
  • Survival or event-history analysis experience.
  • Experience analyzing longitudinal workforce, payroll, or administrative data.
  • Experience with non-random treatment assignment and observational research designs.
  • Experience with U.S. Government-funded research.
  • Experience preparing reproducible analytical code and documentation.
  • Experience presenting statistical findings to non-technical decision makers.
Application Requirements

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