Job Requirements
Fort Meade, MD
Top Secret Polygraph not specified
Early Career (2+ yrs experience)
$125,000 - $145,000
Job Description
Title: Data Scientist / Engineer
Clearance: Top Secret with ability to obtain SCI and CI Poly
Location: Ft. Meade, MD
Role Summary
Agile Defense is seeking a Data Scientist / Engineer to support the design, development, and operational deployment of scalable, AI-enabled data solutions within the Department of Defense’s CDAO ADA IR program. This role is part of a multidisciplinary team integrating advanced analytics, machine learning, and engineering practices into mission-critical environments at CYBERCOM.
You will help shape and deploy data pipelines, pre-processing workflows, feature engineering strategies, and machine learning services within secure, containerized environments. The ideal candidate brings a hybrid of statistical modeling fluency and hands-on software engineering expertise. You will collaborate closely with product managers, full-stack developers, platform engineers, and mission stakeholders to transform raw data into meaningful insights and decision-support tools.
This role requires strong technical communication skills, a collaborative mindset, and experience working in agile environments that value reproducibility, testing, and continuous delivery. Familiarity with cloud-based data platforms such as Databricks, Palantir, or AWS-native data services is highly preferred.
Key Objectives
Objective 1: Design and Maintain Scalable Data Science Services
· Plan, develop, and maintain reusable services for data ingestion, transformation, and feature engineering that support AI/ML workflows.
· Implement core data science capabilities, such as entity resolution, classification, clustering, or prediction, within containerized environments that adhere to CI/CD, version control, and testing standards.
· Collaborate with DevSecOps engineers to integrate services into secure production environments using tools like Databricks, Docker, and Terraform.
· Ensure services meet performance, reliability, and security requirements consistent with DoD enterprise and cloud-native architecture.
Objective 2: Build and Operationalize AI/ML Solutions
· Develop and deploy standalone or embedded ML models for tasks such as decision support, automation, anomaly detection, and pattern recognition.
· Select and implement appropriate modeling techniques using Python, Spark, or cloud-native ML frameworks (e.g., SageMaker, MLflow).
· Maintain reproducibility and interpretability of model outputs to meet mission transparency and audit requirements.
· Package model inference services with well-documented APIs for integration into end-user applications and operational dashboards.
Objective 3: Perform Exploratory Data Analysis and Communicate Insights
· Conduct exploratory data analysis (EDA) to identify trends, gaps, and opportunities within structured and unstructured datasets.
· Develop data visualizations and interpretive summaries that support stakeholder understanding and product team decision-making.
· Translate analytical findings into actionable recommendations using a mix of visual, narrative, and quantitative communication strategies.
· Contribute to the team’s shared library of analysis templates, reusable queries, and analytic workflows to accelerate future delivery.
Objective 4: Collaborate Across Teams to Deliver Mission Impact
· Engage with product managers and mission users to define data and model requirements aligned with operational goals.
· Work closely with engineers to ensure data science components align with technical constraints and deployment patterns.
· Participate in agile sprint planning, retrospectives, and demos, sharing progress and adjusting priorities based on feedback.
· Maintain strong documentation practices that enable handoff, reproducibility, and technical accountability.
Preferred Skills and Experience
· 4+ years of experience in applied data science, machine learning engineering, or data pipeline development.
· Proficient in Python, SQL, and distributed data frameworks (e.g., Spark, Databricks, PySpark).
· Experience developing ML models from training to deployment using industry-standard tools and libraries (e.g., scikit-learn, TensorFlow, XGBoost, MLflow).
· Familiarity with MLOps, API development, and secure cloud-based environments (e.g., AWS, Azure, Palantir Foundry).
· Strong understanding of data validation, model testing, and performance evaluation techniques.
· Experience with data visualization and storytelling using tools such as Tableau, Plotly, or Matplotlib.
· Excellent technical communication skills, with the ability to explain complex concepts to non-technical audiences.
$125,000 - $145,000 a year
Clearance: Top Secret with ability to obtain SCI and CI Poly
Location: Ft. Meade, MD
Role Summary
Agile Defense is seeking a Data Scientist / Engineer to support the design, development, and operational deployment of scalable, AI-enabled data solutions within the Department of Defense’s CDAO ADA IR program. This role is part of a multidisciplinary team integrating advanced analytics, machine learning, and engineering practices into mission-critical environments at CYBERCOM.
You will help shape and deploy data pipelines, pre-processing workflows, feature engineering strategies, and machine learning services within secure, containerized environments. The ideal candidate brings a hybrid of statistical modeling fluency and hands-on software engineering expertise. You will collaborate closely with product managers, full-stack developers, platform engineers, and mission stakeholders to transform raw data into meaningful insights and decision-support tools.
This role requires strong technical communication skills, a collaborative mindset, and experience working in agile environments that value reproducibility, testing, and continuous delivery. Familiarity with cloud-based data platforms such as Databricks, Palantir, or AWS-native data services is highly preferred.
Key Objectives
Objective 1: Design and Maintain Scalable Data Science Services
· Plan, develop, and maintain reusable services for data ingestion, transformation, and feature engineering that support AI/ML workflows.
· Implement core data science capabilities, such as entity resolution, classification, clustering, or prediction, within containerized environments that adhere to CI/CD, version control, and testing standards.
· Collaborate with DevSecOps engineers to integrate services into secure production environments using tools like Databricks, Docker, and Terraform.
· Ensure services meet performance, reliability, and security requirements consistent with DoD enterprise and cloud-native architecture.
Objective 2: Build and Operationalize AI/ML Solutions
· Develop and deploy standalone or embedded ML models for tasks such as decision support, automation, anomaly detection, and pattern recognition.
· Select and implement appropriate modeling techniques using Python, Spark, or cloud-native ML frameworks (e.g., SageMaker, MLflow).
· Maintain reproducibility and interpretability of model outputs to meet mission transparency and audit requirements.
· Package model inference services with well-documented APIs for integration into end-user applications and operational dashboards.
Objective 3: Perform Exploratory Data Analysis and Communicate Insights
· Conduct exploratory data analysis (EDA) to identify trends, gaps, and opportunities within structured and unstructured datasets.
· Develop data visualizations and interpretive summaries that support stakeholder understanding and product team decision-making.
· Translate analytical findings into actionable recommendations using a mix of visual, narrative, and quantitative communication strategies.
· Contribute to the team’s shared library of analysis templates, reusable queries, and analytic workflows to accelerate future delivery.
Objective 4: Collaborate Across Teams to Deliver Mission Impact
· Engage with product managers and mission users to define data and model requirements aligned with operational goals.
· Work closely with engineers to ensure data science components align with technical constraints and deployment patterns.
· Participate in agile sprint planning, retrospectives, and demos, sharing progress and adjusting priorities based on feedback.
· Maintain strong documentation practices that enable handoff, reproducibility, and technical accountability.
Preferred Skills and Experience
· 4+ years of experience in applied data science, machine learning engineering, or data pipeline development.
· Proficient in Python, SQL, and distributed data frameworks (e.g., Spark, Databricks, PySpark).
· Experience developing ML models from training to deployment using industry-standard tools and libraries (e.g., scikit-learn, TensorFlow, XGBoost, MLflow).
· Familiarity with MLOps, API development, and secure cloud-based environments (e.g., AWS, Azure, Palantir Foundry).
· Strong understanding of data validation, model testing, and performance evaluation techniques.
· Experience with data visualization and storytelling using tools such as Tableau, Plotly, or Matplotlib.
· Excellent technical communication skills, with the ability to explain complex concepts to non-technical audiences.
$125,000 - $145,000 a year
group id: 90934119