Principal Data Scientist Insights & Intelligence

Northrop Grumman Northrop Grumman · Aerospace · New York, NY +1 · Data Science

Principal Data Scientist to lead analytical projects, develop statistical models and ML solutions, and build production-grade ML/AI applications for enterprise-wide decision making. Requires strong Python, SQL, Git, and ML deployment experience.

What you'd actually do

  1. Work directly with stakeholders (engineers, program managers, subject matter experts) to scope problems, formulate the right analytical questions, and translate business challenges into rigorous data science approaches
  2. Apply deep analytical thinking to decompose complex problems—critically evaluate data quality and relevance, challenge assumptions, and design methods that address the business need
  3. Develop statistical models, machine learning solutions, and analytical frameworks that deliver actionable insights and drive operational decisions
  4. Build user‑friendly, production‑grade ML/AI applications (e.g., Streamlit, Dash) and analytical artifacts that provide data insights to teams across the enterprise and enable better decision making
  5. Write production-quality Python code and develop analytical pipelines using cloud-based platforms (AWS, Databricks) to support scalable and reproducible data science workflows

Skills

Required

  • Python
  • SQL
  • Git
  • statistical methods
  • machine learning algorithms
  • developing and deploying machine learning models in production environments
  • translate complex business problems into rigorous analytical frameworks
  • problem-solving
  • critical-thinking
  • communication skills
  • deliver compelling, actionable recommendations to non-technical stakeholders
  • ownership and accountability for technical decisions and project outcomes

Nice to have

  • AWS
  • Databricks
  • Streamlit
  • Dash

What the JD emphasized

  • production-grade
  • production-quality
  • production environments
  • production-grade
  • production-quality
  • production
  • production

Other signals

  • production-grade analytics solutions
  • deploy them into production
  • prototype rapidly
  • engineer for production
  • production-quality code
  • production-grade ML/AI applications
  • deploying machine learning models in production environments