DoD-Clearance Data Scientist for AI & Analytics

BOAB Ventures; Washington (District of Columbia); On-site; USD 120,000 - 180,000; Full time

Job summary

BOAB Ventures seeks a Data Scientist/Engineer to design, develop, and operationalize scalable, AI-enabled data solutions within the DoD’s CDAO ADA IR program. You will build data pipelines, feature engineering strategies, and ML services in secure, containerized environments, collaborating with product managers, developers, and mission stakeholders.

The ideal candidate combines statistical modeling with hands-on software engineering, and thrives in agile settings that value reproducibility,

Qualifications

• 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).

Responsibilities

• Design, develop, and deploy scalable AI-enabled data solutions for DoD environments.
• Build and optimize data pipelines, pre-processing, feature engineering, and ML services.
• Collaborate with product managers, developers, and stakeholders in agile, reproducible workflows.
• Work in secure, containerized environments (SCIF) and ensure mission-critical reliability.

Skills

Python

SQL

Spark/Databricks

ML deployment

MLflow

MLOps

API development

AWS

Azure

Data visualization

Communication

Education

Bachelor's degree

Associate's degree

Major certification

Tools

Spark

Databricks

TensorFlow

PyTorch

MLflow

Tableau

Plotly

Matplotlib

Palantir Foundry

Job description

BOAB Ventures seeks a Data Scientist/Engineer to design, develop, and operationalize scalable, AI-enabled data solutions within the DoD’s CDAO ADA IR program. You will build data pipelines, feature engineering strategies, and ML services in secure, containerized environments, collaborating with product managers, developers, and mission stakeholders.

The ideal candidate combines statistical modeling with hands-on software engineering, and thrives in agile settings that value reproducibility,