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Freelance Machine Learning Quant Jobs in Washington, DC

VTG

Data Scientist

Mclean, VA · On-site

Demonstrated experience with quantitative analysis, machine learning, and statistical modeling. * Experience conveying complex data science insights to non-technical audiences * Specific experience ...

VT Group (VTG)

Data Scientist

Mclean, VA · On-site

... quantitative analysis, data science, artificial intelligence or machine learning • Proficiency with tools such as Postgresql/SQL, Python, R, and GitLab • Demonstrated experience with producing ...

Carnegie Mellon University

Data Scientist

Arlington, VA · On-site

Required : • BS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with eight (8) years of experience or equivalent combination of training ...

VTG

Data Scientist

Mclean, VA · On-site

Demonstrated experience with quantitative analysis, machine learning, and statistical modeling. * Experience conveying complex data science insights to non-technical audiences * Specific experience ...

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All JobsMachine Learning Quant JobsFreelance Machine Learning Quant JobsFreelance Machine Learning Quant Jobs in Washington, DC

Freelance Machine Learning Quant information

See Washington, DC salary details

$16

$54

$149

How much do freelance machine learning quant jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for freelance machine learning quant in Washington, DC is $54.03, according to ZipRecruiter salary data. Most workers in this role earn between $27.50 and $69.95 per hour, depending on experience, location, and employer.

What is the difference between Freelance Machine Learning Quant vs Freelance Data Scientist?

AspectFreelance Machine Learning QuantFreelance Data Scientist
CredentialsStrong background in quantitative finance, mathematics, and machine learningBackground in statistics, data analysis, and programming; often less finance-specific
Work EnvironmentFinancial firms, hedge funds, or independent consulting in financeVarious industries including tech, healthcare, marketing, and finance
Industry UsagePrimarily in finance and trading

Freelance Machine Learning Quants focus on applying machine learning techniques to financial markets, often working with trading strategies and risk models. Freelance Data Scientists have a broader scope, working across multiple industries to analyze data, build predictive models, and generate insights. While both roles require strong technical skills, the finance-specific knowledge distinguishes the Freelance Machine Learning Quant from the Freelance Data Scientist.

What are popular job titles related to Freelance Machine Learning Quant jobs in Washington, DC?

For Freelance Machine Learning Quant jobs in Washington, DC, the most frequently searched job titles are:

What job categories do people searching Freelance Machine Learning Quant jobs in Washington, DC look for?

The top searched job categories for Freelance Machine Learning Quant jobs in Washington, DC are:

Freelance Machine Learning Quant jobs near you
Infographic showing various Freelance Machine Learning Quant job openings in Washington, DC as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $112,388 per year, or $54 per hour.

Senior Data Scientist / AI Machine Learning Research Engineer

CACI International, Inc.

Sterling, VA • On-site

$113K - $237K/yr

Full-time

Medical, Retirement, PTO

Re-posted 21 days ago

Job description

Job Title: Senior Data Scientist / AI Machine Learning Research Engineer
Job Category: Science
Time Type: Full time
Minimum Clearance Required to Start: TS/SCI
Employee Type: Regular
Percentage of Travel Required: Up to 10%
Type of Travel: Continental US
* * *
The Opportunity:
CACI has an exciting new opportunity for a Senior AI and Machine Learning Research Engineer. Apply machine learning, statistics, to develop algorithms to solve challenging problems, signal processing, and computer networking domains. In this role, you'll leverage your strong foundation in machine learning, data science, and signal processing to solve complex challenges in the RF domain.
Responsibilities:
1. Strong mathematical foundation in statistics, linear algebra, and calculus with demonstrated ability to understand and implement machine learning algorithms from first principles rather than solely relying on pre-built libraries.
2. Proficiency in designing and building data pipelines, including experience with ETL processes, data warehousing solutions, and optimizing workflows for large-scale data processing.
3. Hands-on experience with cloud-based infrastructure (e.g., AWS, Azure, GCP) for deploying ML solutions, including containerization, orchestration, and CI/CD pipelines for model deployment.
4. Programming expertise in Python and SQL, with experience using data engineering frameworks (e.g., Spark, Airflow) and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
5. Demonstrated experience in establishing ML governance practices, including version control for datasets and models, experiment tracking, model monitoring, and implementing reproducible research principles.
Qualifications:
Required:
  • Master's degree in quantitative field with mathematical underpinnings and at least 7 years' experience.
  • Experience developing models,.
  • Strong background in machine learning, mathematics and statistics.
  • Comfortable using Linux operating systems and commonly used Linux utilities.
  • Must be a US Citizen with the ability to obtain, maintain and/or transfer the required security clearance as dictated by the contract
  • Must have active Top Secret Clearance with the ability to obtain SCI with Polygraph

Desired:
  • Ph.D. in computer science, computer engineering, or machine learning, Statistics, applied mathematics or Physics.
  • Experience applying machine learning to signal processing and/or other time-series data analysis applications.
  • Knowledge of or experience with information theory, probability theory, parametric and non-parametric statistical tests.
  • Familiarity with concepts and techniques associated with adversarial AI and AI/ML assurance.
  • Active Top Secret/SCI clearance preferred.

What You Can Expect:
A culture of integrity.
At CACI, we place character and innovation at the center of everything we do. As a valued team member, you'll be part of a high-performing group dedicated to our customer's missions and driven by a higher purpose - to ensure the safety of our nation.
An environment of trust.
CACI values the unique contributions that every employee brings to our company and our customers - every day. You'll have the autonomy to take the time you need through a unique flexible time off benefit and have access to robust learning resources to make your ambitions a reality.
A focus on continuous growth.
Together, we will advance our nation's most critical missions, build on our lengthy track record of business success, and find opportunities to break new ground - in your career and in our legacy.
Pay Range:
There are a host of factors that can influence final salary including, but not limited to, geographic location, Federal Government contract labor categories and contract wage rates, relevant prior work experience, specific skills and competencies, education, and certifications. Our employees value the flexibility at CACI that allows them to balance quality work and their personal lives. We offer competitive compensation, benefits and learning and development opportunities. Our broad and competitive mix of benefits options is designed to support and protect employees and their families. At CACI, you will receive comprehensive benefits such as; healthcare, wellness, financial, retirement, family support, continuing education, and time off benefits.
The proposed salary range for this position is:
$113,200 - $237,800
CACI is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, age, national origin, disability, status as a protected veteran, or any other protected characteristic.

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