Data Scientist
Accenture Federal Services
Chantilly, VA6d agovia LinkedIn
About the Role
At Accenture Federal Services, the mission is to help the US federal government make the nation stronger and safer, and life better for people. Join a team that is united in a shared purpose to pursue the limitless potential of technology and ingenuity for clients across defense, national security, public safety, civilian, and military health organizations.
Key Responsibilities
- Conduct data analysis using statistical and quantitative approaches.
- Create visualizations that communicate trends, patterns, and insights to support decision-making.
- Develop software solutions that improve monitoring, automation, and management of data pipeline tasks.
- Process and transform complex datasets to produce actionable, data-driven insights.
- Perform data manipulation and execute extract, transform, and load (ETL) procedures.
- Work with various database technologies, including SQL and NoSQL systems.
- Collaborate on projects involving AI/ML technologies and modern data systems.
Requirements & Tech Stack
- Experience with AI/ML technologies.
- Proficiency working with multiple data file types, image files, including geospatial formats, JSON, XML, and others.
- Strong background in quantitative analysis, data visualization, and processing complex datasets.
- Hands-on experience with ETL workflows and data manipulation techniques.
- Working knowledge of SQL and NoSQL database technologies.
- Development experience using Python and additional languages for data cleaning, transformation, and automation.
Qualifications
- TS/SCI or higher clearance level required.
Desired Skills:
- Experience applying Natural Language Processing (NLP) algorithms to extract information from documents.
- Background in enterprise-level Computer Vision, automated data collection, automated reporting, and data standardization best practices.
- Demonstrated expertise in mathematics, statistics, and quantitative analysis.
- Familiarity with analytic techniques such as classification, regression, clustering, dimensionality reduction, and causal modeling.