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Data Scientist

Foreign Resource Development AssociatesVA🇺🇸United StatesPosted 13 Aug 2026

Why This Role Stands Out

This Data Scientist role offers a unique opportunity to develop cutting-edge analytic capabilities and build impactful machine learning models for critical defense and intelligence missions. You'll thrive here if you are a driven mid-senior professional eager to work with diverse datasets, contribute to high-impact projects in a dynamic environment, and continuously expand your skills in areas like signal processing and geospatial analytics. Apply now to join a talented team and make a tangible difference in a mission-focused setting.

Quick Overview

Seniority
Mid Senior
Work mode
On Site
Location
VA, United States
Posted
4 weeks ago
DockerFastAPISQLAWSMachine LearningNLPNumPyOpenCVScikit-learnAgileAzureComputer VisionDeep LearningGitJavaJupyterPandasPythonREST

Job Description

Job Type

Full-time

Description

Position Summary:

FRDA is looking for a Data Scientist to join a multidisciplinary team developing analytic capabilities, capturing workflows, designing predictive models and developing business processes for real-world mission challenges. This role supports defense, intelligence, and federal clients by building machine learning models, data pipelines, and APIs to determine information values, trends, patterns, and relationships-particularly in signal, image, and geospatial domains-and working with structured and unstructured data to generate insights for operations or strategic decision-making.

This role is ideal for someone with a strong interest in high impact, mission-focused work in a fast-paced DOD environment.

This role is onsite in Springfield, VA.

Primary Duties and Responsibilities:
  • Design, build, and maintain robust data pipelines for structured and unstructured data.
  • Accurately determine feasibility of aggregating, transporting, storing, and safeguarding the data.
  • Develop and deploy Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Computer Vision algorithms and models for signal processing, geospatial analytics, object detection, anomaly detection, and to enable prediction.
  • Analyze data to identify insights that can be gleaned from the data by determining information values, trends, patterns, and relationships within the data.
  • Clean, process, and format raw data into actionable, mission-ready outputs.
  • Support edge-computing constraints and adapt models to operate in austere or bandwidth-constrained settings.
  • Work within modern development stacks including Python, Java script Application Programming Interface (API), Docker, Visual Studio (VS) Code, Jupyter, Gitlab, Git, and Continuous Integration/Continuous Delivery (CI/CD) pipelines.
  • Collaborate with analysts, engineers, and mission leads to prototype and iterate on solutions rapidly.

Requirements

Minimum Qualifications:
  • Active TS/SCI clearance.
  • Bachelor's degree in Data Science, Computer Science, Engineering, or related field.
  • A minimum of four (4) years of experience in data science, machine learning, or analytics in a production or research setting.
  • Strong mathematics background in Statistics, Graph Theory, Probability, or Calculus to develop and test algorithms and models .
  • Proficiency with Python (NumPy, Pandas, Scikit-learn, OpenCV), SQL, R, and Git.
  • Experience in building end-to-end analytic solutions-from data ingestion to deployment-and data visualization tools.
  • Exposure to signal processing, computer vision, or geospatial analytics projects.

Preferred Qualifications:
  • Master's degree or certifications in data engineering, machine learning, or related areas.
  • Familiarity with FastAPI, Python, Java script Application Programming Interface (API), Docker, Visual Studio (VS) Code, Jupyter, Gitlab, Git, cloud services (e.g. AWS, Azure), and REST APIs.
  • Software engineering and/or application development to support automating processes, dashboards, and workflows.
  • Experience supporting national security, defense, or intelligence community use cases.
  • Exposure to edge-AI environments or low-latency ML deployments.
  • Background in Agile environments with rapid prototyping capabilities.

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