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Data Scientist; AI​/ML SME

Job in Chantilly, Fairfax County, Virginia, 22021, USA
Listing for: Amatriot Group, LLC
Full Time position
Listed on 2026-10-29
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 130000 - 200000 USD Yearly USD 130000.00 200000.00 YEAR
Job Description & How to Apply Below

Location: Chantilly, VA
Security Clearance: Active TS or higher [Required]
Job Type: Full-Time
Target Salary Range*:$130,000 - $200,000

* This represents the potential salary range for this position depending on education level, years of experience and/or certifications in addition to other position specific requirements which may impact salary

Position Overview

The Data Scientist (AI / ML) develops machine learning models, advanced analytics solutions, and generative AI capabilities supporting intelligent products and data-driven decision-making. This role spans the full model lifecycle, from data exploration and feature engineering through validation and refinement. Working with data engineering, product, and MLOps teams, the scientist translates complex algorithmic outcomes into production-ready systems and actionable stakeholder insights.

Key Responsibilities Machine Learning and Advanced Analytics
  • Develop machine learning models and advanced analytics solutions.
  • Perform data exploration, feature engineering, model validation, and refinement.
  • Prototype scalable predictive, classification, and optimization solutions.
Generative AI Development
  • Integrate modern generative AI techniques into intelligent products and analytical solutions.
  • Perform large language model (LLM) fine-tuning and prompt engineering.
  • Develop retrieval-augmented generation (RAG) workflows.
Experimental Design and Model Governance
  • Establish rigorous experimental designs to support model development and evaluation.
  • Monitor model fairness and governance throughout the model lifecycle.
Collaboration and Production Integration
  • Partner with data engineering, product, and MLOps teams to translate algorithmic outcomes into production-ready systems.
  • Communicate complex analytical results as actionable stakeholder insights.
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