Senior Data Science Engineer

GrammaTech, Inc.•Laurel, MD

1d•$180,000 - $220,000

About The Position

We are seeking a highly experienced Senior Data Science Engineer to support the development of advanced compliance analytics, automation tools, and machine learning solutions within a mission-focused environment. This role will be responsible for designing, prototyping, implementing, and monitoring AI/ML-driven capabilities that enhance compliance processes, improve operational efficiency, and support modernization initiatives. Clearance Required : Active TS/SCI with Polygraph

Requirements

12+ years of Software Engineering experience supporting programs of similar scope, type, and complexity.
Bachelor's degree in Computer Science or related field (4 additional years of relevant experience may substitute for a degree).
Strong experience with Java, Scala, and Python.
Experience with Lucene, JEXL, SQL, JSON, and Linux development environments.
Hands-on experience building, deploying, and monitoring machine learning models.
Experience with Random Forest algorithms and feature engineering.
Proficiency with Scikit-learn, Jupyter Notebooks, Ghostmachine (Map/Reduce), and GM Learn.
Experience using Git/GitLab, Bitbucket/Stash, IntelliJ/Eclipse, Jira, and Confluence.
Strong documentation, communication, and technical review skills.
Knowledge of agency compliance standards and corporate systems.

Nice To Haves

Data Science background or advanced analytics experience.
Experience with AWS cloud technologies and Apache Spark.
Experience implementing production ML systems.
Knowledge of Agentic AI, Large Language Models (LLMs), and emerging AI technologies.
Experience with advanced machine learning techniques and model development.

Responsibilities

Develop, enhance, and maintain compliance analytics and business process automation tools.
Research, evaluate, and prototype machine learning and AI solutions to support compliance modernization efforts.
Build, deploy, and monitor machine learning models, including Random Forest models and feature engineering pipelines.
Develop performance metrics, monitoring capabilities, and data flow solutions to ensure operational effectiveness.
Conduct exploratory data analysis (EDA) and identify opportunities for process improvement.
Collaborate with cross-functional teams to integrate AI/ML solutions into production environments.
Create and maintain technical documentation, design artifacts, and implementation guides.
Ensure adherence to agency standards, policies, and compliance requirements.