Data Scientist

CALIBRE Systems, Inc.•Arlington, VA

22h•Onsite

About The Position

CALIBRE is seeking an experienced Data Scientist to join a data team driving advanced analytics initiatives for Army analysis and budgeting processes. The Data Scientist will deliver production-ready data products, analytics, and decision-support applications using Army Vantage and Department of War (DoW) War Data Platform (WDP) capabilities. This role converts mission and business questions into governed data pipelines, analytical models, semantic data structures, dashboards, and workflow applications that enable timely, trusted decisions. This is a hands-on delivery role. The successful candidate will partner with mission owners, data stewards, engineers, analysts, and platform teams to integrate authoritative data, improve data quality, build reusable analytics, and transition solutions into sustained operations.

Requirements

Experienced Data Scientist
Proficiency in Python
Proficiency in SQL
Proficiency in PySpark/Spark
Experience with Army Vantage and Department of War (DoW) War Data Platform (WDP) capabilities
Experience building and maintaining scalable data pipelines
Experience developing and maintaining data models, transformations, workflows, dashboards, and user-facing applications
Experience designing analytical methods (forecasting, anomaly detection, optimization, classification)
Experience developing visualizations and dashboards
Experience with Power BI, Tableau, Qlik, or comparable approved tools
Experience implementing data-quality checks, lineage documentation, validation tests, model-performance monitoring, and reproducible analytic workflows
Knowledge of platform, data-governance, cybersecurity, access-control, and release requirements
Experience with Agile delivery practices
Experience training end users and analysts
Experience creating technical documentation, data dictionaries, user guides, and sustainment handoffs
Experience communicating findings to technical and non-technical stakeholders

Nice To Haves

Experience with ontology-aligned objects

Responsibilities

Partner with functional stakeholders to define decision problems, success measures, data requirements, and minimum viable analytic products.
Ingest, profile, clean, transform, and integrate structured and unstructured data from authorized enterprise and legacy sources.
Build and maintain scalable data pipelines, curated datasets, and reusable analytical data products using Python, SQL, PySpark/Spark, and platform-native capabilities.
Develop and maintain Vantage/Foundry data models, ontology-aligned objects, transformations, workflows, dashboards, and user-facing applications.
Design mission-appropriate analytical methods, including forecasting, anomaly detection, optimization, or classification when supported by data quality and operational need.
Develop clear visualizations, dashboards, and executive-ready readouts using Vantage/Foundry tools and as required, Power BI, Tableau, Qlik, or comparable approved tools.
Implement data-quality checks, lineage documentation, validation tests, model-performance monitoring, and reproducible analytic workflows.
Apply platform, data-governance, cybersecurity, access-control, and release requirements; coordinate with data owners and stewards to ensure proper use of authoritative data.
Use Agile delivery practices: refine requirements, estimate work, demonstrate increments, document solutions, and manage technical debt.
Train end users and analysts; create concise technical documentation, data dictionaries, user guides, and sustainment handoffs.
Communicate findings, limitations, assumptions, and recommended actions clearly to both technical teams and senior nontechnical stakeholders.
Deliver [2–4] production-ready data products supporting validated mission decisions.
Reduce manual data preparation or reporting time by 50% for targeted workflows.
Establish documented data lineage, data-quality rules, and sustainment procedures for all delivered products.
Achieve stakeholder acceptance and measurable adoption of dashboards, workflows, or analytic applications.
Transition of reusable code, documentation, and operating procedures to the designated sustainment team.