Data Scientist (All Levels)

Adaptic•Annapolis Junction, MD

70d

About The Position

At Adaptic, we don’t just analyze data—we turn it into mission-critical insight. Our teams build advanced analytics, machine learning models, and data-driven solutions that support real-world operations across the DoD and Intelligence Community. Whether you’re early in your career or leading analytic efforts and shaping strategy, we’re looking for Data Scientists at all levels who want to tackle complex problems, work with meaningful data, and see their work make an impact. Let us help align you to the right role based on your strengths and interests. You’ll work across the full data science lifecycle—exploration, modeling, validation, and operationalization—on problems that demand both technical depth and creative thinking.

Requirements

All positions require an active TS/SCI with polygraph
Experience with Python, R, or similar languages used for data analysis and modeling
Strong foundation in statistics, machine learning, or data analysis techniques
Experience working with large, complex, or imperfect datasets
Ability to communicate technical findings clearly to both technical and non-technical audiences
A collaborative mindset with the ability to work independently and as part of a team
A degree in Data Science, Computer Science, Statistics, Mathematics, or a related field (or equivalent experience)

Nice To Haves

Experience with cloud environments (AWS), big data tools, or distributed processing frameworks
Familiarity with ML pipelines, model deployment, or MLOps concepts
Exposure to graph analytics, NLP, or AI-driven systems
Experience working in mission or high-performance computing environments

Responsibilities

Developing machine learning, statistical, data mining, and graph-based models to extract insight from complex datasets
Exploring multiple approaches and selecting models based on performance, scalability, and mission need
Building experiments and generating synthetic data when real-world data is limited or incomplete
Creating visualizations and reports that clearly communicate findings and drive decision-making
Translating manual or ad hoc analysis into automated, repeatable analytic solutions
Engineering features and preparing data for use in machine learning workflows
Tuning models and parameters to optimize performance across varying datasets
Evaluating model effectiveness using standard techniques (e.g., cross-validation, ROC curves, confusion matrices)
Partnering with software engineers and cloud teams to deploy analytics into production environments
Contributing to scalable solutions that can handle large, evolving datasets
Supporting or leading analytic efforts, depending on your experience level