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 . Please Note: This position is contingent upon contract award.

The War Data Platform (WDP) is a key initiative within the U.S. Department of War's (DoW) AI-First strategy introduced in early 2026. The WDP focuses on operational warfighting data and aims to accelerate the deployment of artificial intelligence (AI) on the battlefield. The WDP extends to Unclassified, Secret, and Top Secret environments, and supports collaboration between Combatant Commands, Joint Staff directorates, Senior Executive Service leaders, and operational analysts.

The Data/ML Scientist SME is a principal-level subject matter expert responsible for architecting and sustaining the machine learning-driven data quality capabilities that underpin the WDP Core Integration enterprise, ensuring that 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. This role serves as the authoritative technical voice on ML-based data quality monitoring, anomaly detection, and analytic readiness across all WDP security enclaves, and operates in close collaboration with data engineering, platform, cybersecurity, and AI integration teams to drive continuous improvement across the program's full data lifecycle.

Architects and optimizes machine learning-driven data quality capabilities across Unclassified and NIPR, Secret and SIPR, and Top Secret and JWICS environments to advance War Data Platform (WDP) Core Integration enterprise 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 War Data Platform (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.

Required Skills

Current Secret security clearance with the ability to obtain and maintain a Top Secret (TS) security clearance with Sensitive Compartmented Information (SCI).
12 or more years of progressively responsible experience in data science, machine learning engineering, or a closely related field, with demonstrated expert-level proficiency 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 experience with 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 GovCloud 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 tools such as Tableau, Power BI, or Databricks SQL, and to translate complex technical findings into actionable recommendations for senior leaders and cross-functional engineering teams.
Strong problem-solving and decision-making capabilities, with a proven ability to weigh the relative costs and benefits of potential actions and identify the most appropriate solution.
Highly developed interpersonal and oral/written communication skills, with the ability to effectively and professionally interact with a diverse set of stakeholders (from peers to end-users to executive management).

Desired Skills

Active Top Secret (TS) security clearance with Sensitive Compartmented Information (SCI) eligibility.
Advanced degree (Master's or Doctorate) in Data Science, Computer Science, Applied Mathematics, Statistics, or a closely related quantitative field; relevant professional certifications such as AWS Certified Machine Learning Specialty, Databricks Certified Associate Developer for Apache Spark, or equivalent credentials are also highly valued.
Prior experience contributing to or leading data science and ML operations on DoW, intelligence community, or defense AI/ML programs such as CDAO, Advana, or Maven Smart System, including familiarity with ML lifecycle governance, model registry management, and MLOps toolchain integration in classified environments.
Familiarity with AI risk assessment, model explainability, and responsible AI practices applicable to DoW mission systems, including experience supporting continuous Authority to Operate processes and data governance frameworks aligned to DoW data strategy objectives.
Experience mentoring data engineers and junior data scientists, contributing to program-level data quality governance frameworks, and leading analysis-of-alternatives efforts that shape enterprise tooling investment decisions.
Experience with or strong interest in designing semantic data interfaces, metadata layers, or API-based data products that enable automated workflows, AI assistants, and emerging agentic AI capabilities.

ECS Federal LLC is an equal opportunity employer and does not discriminate or allow discrimination on the basis any characteristic protected by law. All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, or local jurisdiction law.

Everforth ECS is the federal segment of Everforth , a $4B global organization with over 10,000 employees. Our nearly 3,500 professionals deliver advanced technology solutions in data and AI, cybersecurity, and enterprise transformation, serving defense, intelligence, and federal civilian agencies.

Our work powers mission-critical outcomes, strengthens technology partnerships, and creates meaningful opportunities for our people. We are defined by a commitment to excellence in delivery, a culture of innovation, and an environment where talent can thrive and grow.

We value:

• Attracting and developing top talent and high-performing teams
• Fostering a culture that is engaging, accountable, and mission-driven