Why this role stands out
This remote Data Scientist Associate role at SAIC offers a competitive salary and the chance to contribute to cutting-edge AI technology integration for the US government. You'll thrive here if you enjoy leveraging advanced analytics and AI models to solve complex problems, with ample opportunities for learning and exploration within a supportive team. Apply to join this dynamic environment and advance your career in data science.
Quick overview
Job description
Location: Upper Marlboro, MD, US
Date Posted: 2026-11-12
Category: Information Technology
Subcategory: Data Scientist
Schedule: Full-Time
Shift: Day Job
Travel: Yes - 25% of the time
Minimum Clearance Required: None
Clearance Level Must Be Able to Obtain: Public Trust
Potential for Remote Work: ORA_HYBRID
Description
SAIC is seeking a Data Scientist Associate with a strong background in statistical data analysis using modern analytic tools and frameworks. Experience with techniques and platforms to create, manage, manipulate, and draw insight from large datasets is desired. The candidate will join the dynamic team at the Identity and Data Sciences Laboratory (IDSL). The IDSL is tasked with evaluating how new AI technologies can be best integrated into operational use-cases across the US government. We are investigating how AI systems can improve process efficiency and effectiveness, be optimally teamed with human operators, and detect fraud. Our engineers enjoy a great work-life balance and work in an environment that promotes learning and exploration.
Responsibilities:
- Work with senior data scientists to perform data management and analysis tasks.
- Develop and apply extract, transform, load (ETL) data pipelines to create analytic datasets.
- Perform exploratory data analyses (EDA) on analytic datasets.
- Apply AI models to datasets for AI evaluation, data enrichment, prediction, or generation.
- Work with senior scientists and engineers to develop statistically sound designs of experiment, defining and testing hypotheses.
- Work with senior scientists and engineers to plan and execute comprehensive statistical analyses to generate quantitative results.
- Maintain attention to detail and quality standards to ensure results are accurate and withstand technical scrutiny.
- Contribute to ongoing data collection activities.
Qualifications
Required:
- A bachelor's degree in mathematics, statistics, data science, or a comparable quantitative science,
- 0-2 years applicable professional experience
- Ability to obtain and maintain a public trust requiring ship.
- Proficiency in programming statistical analyses, machine learning, and/or data mining (R, Python, or similar).
- Proficiency in database technologies (SQL, NoSQL).
- Familiarity with source control (e.g., Git).
- Familiarity with common data formats (JSON, Parquet, XML, etc.).
Desired:
- Experience with techniques and platforms to manage, manipulate, and draw insight from large datasets (Spark, Hadoop, Databricks, etc.).
- Understanding of scaling and performance of distributed/cloud systems (AWS).
- Understanding of machine vision, generative AI, machine learning, automation, and scripting.
- Strong communication skills, with the ability to present findings and recommendations to both technical and non-technical audiences.
Target salary range: $80,001 - $120,000. The estimate displayed represents the typical salary range for this position based on experience and other factors.
SAIC is a premier technology integrator providing full life cycle services and solutions in the technical, engineering, intelligence, and enterprise information technology markets. SAIC is Redefining Ingenuity through its deep customer and domain knowledge to enable the delivery of systems engineering and integration offerings for large, complex projects. SAIC's approximately 15,000 employees are driven by integrity and mission focus to serve customers in the U.S. federal government. Headquartered in Reston, Virginia, SAIC has annual revenues of approximately $4.5 billion. For more information, visit saic.com. For information on the benefits SAIC offers, see .
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