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Assistant Machine Learning Quant Jobs in Virginia

Hirekeyz Inc

Sr. Machine Learning Engineer

Fort Belvoir, VA · On-site

$118.20K - $162.30K/yr

Role: Sr. Machine Learning Engineer Location: Ft. Belvoir, VA (On-site with Hybrid Option) Duration ... DOD Top Secret Clearance (Must) As a consultant, will be working to assist a DoD U.S. Army Command ...

Hirekeyz Inc

Lead Machine Learning Engineer

Fort Belvoir, VA · On-site

$115.90K - $152.70K/yr

Role: Lead Machine Learning Engineer Location: Ft. Belvoir, VA (On-site with Hybrid Option ... assist a DoD U.S. Army Command to create cybersecurity solutions working with cloud-based ...

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Assistant Machine Learning Quant information

What are the key skills and qualifications needed to thrive as an Assistant Machine Learning Quant, and why are they important?

To thrive as an Assistant Machine Learning Quant, you need strong quantitative skills, a background in statistics or mathematics, and typically a degree in a STEM field. Familiarity with programming languages such as Python or R, experience with machine learning frameworks, and knowledge of financial modeling tools are essential. Strong problem-solving abilities, attention to detail, and effective communication are standout soft skills in this role. These competencies enable accurate model development, efficient data analysis, and clear collaboration with team members in high-stakes financial environments.

How does an Assistant Machine Learning Quant typically collaborate with senior quants and data scientists on projects?

As an Assistant Machine Learning Quant, you will often work closely with senior quantitative researchers and data scientists by supporting model development, data preprocessing, and feature engineering tasks. You may contribute to brainstorming sessions, implement prototypes, and assist in backtesting trading strategies or risk models. This collaborative environment provides valuable mentorship opportunities and exposure to best practices in quantitative analysis and machine learning within the finance industry. Effective communication and a willingness to learn from senior team members are key to success in this role.

What are Assistant Machine Learning Quants?

Assistant Machine Learning Quants are entry-level professionals in quantitative finance who support senior quants by applying machine learning techniques to analyze financial data, build predictive models, and develop trading strategies. Their responsibilities often include data cleaning, feature engineering, model selection, and performance evaluation. They work closely with quantitative researchers and traders to improve algorithmic trading systems and risk management processes. This role typically requires strong programming skills, a solid understanding of machine learning concepts, and familiarity with financial markets.
What are the most commonly searched types of Machine Learning Quant jobs in Virginia? The most popular types of Machine Learning Quant jobs in Virginia are:
What are popular job titles related to Assistant Machine Learning Quant jobs in Virginia? For Assistant Machine Learning Quant jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Assistant Machine Learning Quant jobs in Virginia look for? The top searched job categories for Assistant Machine Learning Quant jobs in Virginia are:
What cities in Virginia are hiring for Assistant Machine Learning Quant jobs? Cities in Virginia with the most Assistant Machine Learning Quant job openings:
Assistant Machine Learning Quant jobs near you
Infographic showing various Assistant Machine Learning Quant job openings in Virginia as of May 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Senior Data Scientist / AI Machine Learning Research Engineer

CACI bv

Sterling, VA • On-site

Full-time

Posted 6 days ago

Job description

Job Summary:
CACI is seeking a Senior Data Scientist / AI Machine Learning Research Engineer to apply machine learning and statistics to develop algorithms for challenging problems in signal processing and computer networking. The role involves leveraging expertise in machine learning, data science, and signal processing to solve complex challenges in the RF domain.
Responsibilities:
• 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.
• Proficiency in designing and building data pipelines, including experience with ETL processes, data warehousing solutions, and optimizing workflows for large-scale data processing.
• 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.
• 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).
• 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.
Preferred:
• 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.
Company:
CACI levert, implementeert en beheert bedrijfskritische oplossingen voor het Hoger Onderwijs: het StudentInformatieSysteem OSIRIS en LISA voor zaakgericht werken. Founded in 1997, the company is headquartered in Amsterdam, NLD, with a team of 51-200 employees. The company is currently Growth Stage.

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