Data Scientist

CALIBRE Systems, Inc.; Arlington (VA); On-site; USD 120,000 - 150,000; Full time

Job summary

CALIBRE Systems, Inc. in Arlington, VA seeks an experienced Data Scientist to drive advanced analytics for Army analysis and budgeting.

You will deliver production-ready data products, analytics, and decision-support applications using Army Vantage and DoW War Data Platform capabilities. This hands-on role partners with mission owners, data stewards, engineers, analysts, and platform teams to ingest, clean, transform, model, and visualize data; build scalable pipelines and reusable analytics;

Qualifications

• Bachelor's degree required or equivalent experience in a quantitative field.
• Experience building production-grade analytics and data products.
• Strong skills in data wrangling, modeling, and governance.

Responsibilities

• Ingest, profile, clean, transform, and integrate structured and unstructured data.
• Build and maintain scalable data pipelines and curated datasets.
• Develop dashboards and user-facing applications using Vantage/Foundry, Power BI, Tableau, or equivalents.
• Apply data-governance, security, and release practices.
• Collaborate with mission owners, data stewards, engineers, analysts, and platform teams.
• Communicate findings clearly to technical and nontechnical stakeholders.

Skills

Python

SQL

PySpark

Data modeling

Dashboarding

Agile

Stakeholder management

Education

Bachelor's degree in a relevant field

Tools

Vantage/Foundry

Power BI

Tableau

Job description

CALIBRE is an employee-owned mission focused solutions and digital transformation company. 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.

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.