Data Scientist

Overview

Apogee is seeking a Data Scientist (DS) to support the U.S. Army Space and Missile Defense Command (USASMDC). This role applies data science, statistics, and automation to mission analytics, helping operational and intelligence teams process large, complex datasets and convert them into actionable insights in support of space and missile defense priorities.

The Data Scientist partners with analysts and technical stakeholders to identify high‑value analytic problems, engineer and curate datasets, and develop reproducible pipelines and models to automate workflows and enrich mission data. Responsibilities include data exploration and feature engineering; building and evaluating statistical/ML approaches (as appropriate); developing scripts, tools, and dashboards to support analysis and reporting; and communicating results through clear documentation and briefings.

The DS also supports data governance and quality efforts, ensures methods are transparent and repeatable, and transitions solutions into operational use in coordination with Government leads and mission users.

This is a full‑time opportunity at Petterson Space Force Base (PSFB), CO.

**** Contingent Upon Contract Award****

• Demonstrate experience and knowledge of computer science concepts, data architecture, intelligence practices, and managing data in a big‑data environment.
• Demonstrate expert knowledge of Python, and Jupyter Notebooks and/or Jupyter Labs.
• Author cogent and logical scripts using Python and other applicable languages in a virtual environment using common Integrated Development Environments (IDE) such as VS Code, Spyder, PyScript, or Jupyter Notebooks.
• Develop and use advanced software programs, algorithms, query techniques, models to solve complex intelligence problems, and automated processes to normalize, integrate, and evaluate data.
• Debug existing and future Python code; refactor legacy code to ensure continued security, functionality, and compatibility.
• Document and block‑comment all code to ensure recoverability and error‑checking, and enhance reading, checking, and maintaining code in accordance with common data science and coding standards, such as PEP‑8 for Python,5 or using style‑guide features embedded in common IDE applications, such as Spyder, VS Code, PyScript or others upon approval by the Government.
• Collaborate across multi‑discipline teams to ensure connectivity between various data sources and business problems.
• Identify meaningful insights, interpret, and communicate findings, plus make recommendations to stakeholders.
• Analyze requirements and evaluate technologies for data science capabilities including Natural Language Processing, Machine Learning, predictive modeling, statistical analysis, and hypothesis testing.
• Maintain awareness of emerging analytics and big‑data technologies.
• Complete required course NSA NETA
  1400 (Technology Fundamentals for Analysis); recommended completion of any of the following NSA Courses: NETA
  2402/NETA
  2108 (Analysis II), RPTG
  2238, RPTG
  2235, RPTG
  3225, RPTG
  3222 (Basic Analytical Reporting) or equivalent curriculum.
• Complete recommended certifications:
  Data Science Council of America (DASCA) certifications, such as Associate Big Data Engineer (ABDE), Associate Big Data Analyst (ABDA), and Senior Data Scientist (SDS);
  Google Data Analytics Professional Certificate; IBM Data Science Professional Certification.

Minimum Experience:

Citizenship: Must be a US citizen Clearance: Must have and be able to maintain a Top Secret (TS) with Sensitive Compartmented Information (SCI) adjudication Education: Bachelors Degree Years of

Experience:

7+ years experience in data science/analytics engineering/software development in big‑data environments.

Additional

Experience:

• Expert Python skills; heavy use of Jupyter Notebooks/Labs and common IDEs (e.g., VS Code/Spyder).
• Proven ability to build and sustain automated data workflows (ingest/normalize/integrate/enrich/evaluate) using algorithms, queries, and models.
• Strong SDLC skills: debugging, refactoring legacy code, and maintaining secure/compatible codebases.
• Code quality discipline: clear documentation/block comments and…