Data Scientist
Company: Booz Allen Hamilton
Location: Langley Forest, VA (Remote)
Type: Full-time
Remote: Yes
Posted: 2026-05-12
About this role
Job Number: R0238793
Data Scientist
The Opportunity
:
As a data scientist, you’re joining a team dedicated to empowering decision-makers through data science, machine learning, and semantic technologies in mission-critical environments. We deliver cutting-edge solutions to enhance the interoperability, usability, and standardization of structured and unstructured data systems. Our work drives operational excellence and enables informed decision-making across complex domains while tackling emerging global challenges.
We are seeking an innovative and highly skilled Data Scientist to support advanced data interoperability efforts within dynamic operational environments. In this role, you will partner with technical and operational teams to identify challenges, create scalable solutions, and optimize end-to-end data workflows to meet mission objectives. You’ll work closely with clients to understand their questions and needs, and then dig into their data-rich environments to find the pieces of their information puzzle. You’ll guide teammates and lead the development of algorithms and systems. You’ll use the right combination of tools and frameworks to turn sets of disparate data points into objective answers to advise your clients as they make informed decisions. Ultimately, you’ll provide a deep understanding of the data, what it all means, and how it can be used.
Join us. The world can’t wait.
You Have:
- 5+ years of experience with Python-based data analysis, including anomaly detection and techniques for exploratory data analysis (EDA)
- 5+ years of experience designing and implementing solutions with APIs and SDKs, including data integration
- Experience structuring, curating, and normalizing disparate data sources, including in complex settings with operational constraints
- Knowledge of developing data models such as semantic taxonomies or ontologies, including cross-system interoperability and machine-to-machine com...