MASINT Data Analyst

Vantor•Reston, VA

13d•$116,000 - $169,400

About The Position

Vantor is hiring a MASINT Data Analyst to support a critical, high-visibility Intelligence Community mission. The analyst will contribute subject-matter expertise in data analytics and MASINT analytical development, collaborating with senior data scientists and counterparts across the Intelligence Community (IC) and Defense Intelligence Enterprise (DIE). The role includes preparing and analyzing structured and unstructured datasets, supporting data pipelines and downstream modeling and LLM/NLP workflows, and communicating analytical results to technical and nontechnical audiences. Previous on-site experience supporting the DoD and intelligence community is highly desirable. Applicants must be U.S. citizens and hold an active U.S. Government TS/SCI security clearance with CI Polygraph. Cleared employment is contingent upon clearance verification. Some roles may also be subject to U.S. export control laws requiring U.S. person status.

Requirements

Active TS/SCI security clearance with CI Polygraph.
Bachelor’s degree in data science, computer science, engineering, statistics, GIS, or a related discipline; the degree may be substituted with an additional two years of experience.
Three years of professional experience in data analytics or data engineering roles.
Proficiency in Python and SQL.
Experience with data-wrangling libraries such as pandas or PySpark, and with APIs or batch-processing tools.
Experience working with SQL and relational databases.
Understanding of data modeling, metadata management, and data-governance principles.
Proficiency with Tableau or similar dashboard tools.
Exposure to cloud platforms such as AWS, Azure, or GCP, especially data-related services.
Strong attention to detail and documentation practices.
Ability to translate stakeholder questions into clear analytical requirements, metrics, reports, and visualizations.
Ability to communicate findings through written products, dashboards, briefings, and data visualizations.
U.S. citizenship.

Nice To Haves

Master’s degree in a technical field such as data science, computer science, or engineering.
Experience working with MASINT and TECHINT data.
Experience using Clairvoyant or similar orchestration platforms to manage and monitor data pipelines.
Familiarity with container orchestration tools such as Docker, Rancher, or Kubernetes.
Basic knowledge of LLM and NLP concepts, including using pre-trained models for tasks such as summarization or keyword extraction.
Basic familiarity with GIS tools and data.
Experience with graph data science algorithms and graph analytics.
Knowledge of enterprise architecture, metadata standards, or master data management.
Experience working in agile teams or cross-functional technical environments.
Understanding of responsible AI practices and basic data ethics.
Previous on-site experience supporting the DoD and intelligence community.

Responsibilities

Collaborate with senior data scientists to prepare, clean, and process structured and unstructured datasets for analysis.
Use Python, SQL, and scripting tools to automate routine data-preparation, data-quality, and reporting tasks.
Query, integrate, and analyze data from relational databases, approved APIs, flat files, and enterprise data platforms.
Document source-data issues, business rules, transformations, and validation needs for data engineering teams.
Identify data-quality issues, inconsistencies, and gaps, and coordinate with technical teams to resolve them.
Assist with developing and deploying data pipelines alongside data engineering and DevOps teams.
Support data tagging, metadata capture, and dataset curation for knowledge-management and LLM-enabled workflows.
Implement basic statistical analyses, visualizations, and reporting for exploratory data analysis and hypothesis validation.
Maintain detailed documentation of data preparation methods, scripts, and pipeline configurations.
Support integration of data into downstream modeling and LLM/NLP workflows.
Assist in tagging or curating training datasets for LLM models and gain exposure to LLM-supported knowledge-management systems.
Develop recurring reports, ad hoc analyses, and data visualizations for operational and leadership decision-making.
Communicate analytical findings, assumptions, limitations, and recommendations to technical and nontechnical audiences.