Data​/ML Scientist SME

Job Description

Everforth ECS is seeking a Data/ML Scientist SME to work in the National Capital Region covering the Pentagon, Falls Church, and Fairfax. This position is contingent upon contract award.

The War Data Platform (WDP) is a key initiative within the U.S. Department of War’s AI-First strategy introduced in early 2026. The WDP focuses on operational war fighting data and aims to accelerate the deployment of artificial intelligence (AI) on the battlefield, extending to Unclassified, Secret, and Top Secret environments.

The Data/ML Scientist SME is a principal-level subject matter expert responsible for architecting and sustaining machine learning-driven data quality capabilities that underpin the WDP Core Integration enterprise. This includes ensuring mission data serving Combatant Commands, Joint Staff elements, and interagency partners meets the accuracy, completeness, and timeliness standards required for AI-enabled warfighter decision advantage.

• Architects and optimizes machine learning-driven data quality capabilities across Unclassified, NIPR, Secret, SIPR, Top Secret, and JWICS environments to advance WDP Core Integration data readiness.
• Designs, builds, and maintains data quality monitoring tools using Apache Spark, Databricks, Python validation frameworks, Great Expectations, Delta Live Tables, and cloud-native observability services to evaluate accuracy, completeness, timeliness, lineage fidelity, and schema consistency across ingest pipelines and medallion zone storage layers.
• Develops automated anomaly detection methods, statistical drift monitoring models, and ML-based pattern recognition workflows that identify deviations in mission data supporting Combatant Commands, Joint Staff elements, and interagency partners.
• Conducts analysis of alternatives on data tooling solutions, benchmarks tool performance metrics, and recommends enhancements that increase throughput, scalability, and operational reliability across all enclaves.
• Implements dashboards using Tableau, Power BI, and Databricks SQL to visualize operational data health, tool performance indicators, and mission impact assessments for senior leaders and engineering teams.
• Integrates outputs into continuous improvement cycles by collaborating with data engineering, cybersecurity, platform, and artificial intelligence teams to strengthen WDP Core Integration data governance and enterprise resilience.
• Produces technical reports, engineering findings, data quality scoring models, and modernization roadmaps that drive measurable improvements in analytic readiness, model performance, and decision superiority across the Department of War.
• Performs other duties as assigned.

• Current Secret security clearance with the ability to obtain and maintain a Top Secret (TS) security clearance with Sensitive Compartmented Information (SCI).
• 12+ years of progressively responsible experience in data science, machine learning engineering, or a closely related field, with expert-level proficiency in designing and operationalizing ML-driven data quality and analytics capabilities in enterprise or multi-enclave defense environments.
• Experience or expertise in Bayesian statistical frameworks, including Bayesian causal inference methods for reasoning under uncertainty, evaluating intervention effects, and supporting decision making in complex operational environments.
• Expert proficiency in Python-based data science and ML frameworks, including Apache Spark, Databricks, Great Expectations, and Delta Live Tables for large-scale pipeline validation, anomaly detection, statistical drift monitoring, and medallion architecture data quality management.
• Demonstrated experience building and deploying ML models, automated validation workflows, and data observability solutions in DoW-compliant cloud environments such as AWS Gov Cloud or AWS Secret Region, including operations across NIPRNet, SIPRNet, and JWICS security enclaves.
• Proven ability to design and deliver executive-facing data quality dashboards and mission impact assessments using Tableau, Power BI, or Databricks SQL, translating complex technical findings into actionable recommendations…