Sr. Principal Data Scientist - Machine Learning Engineer

Northrop Grumman seeks a Sr. Principal Data Scientist / Machine Learning Engineer to build production-grade ML/AI applications, cloud infrastructure, and MLOps that enable data-driven decision making across programs.

Responsibilities

• Collaborate with engineers, program managers, and subject matter experts to define problems, assess constraints, and iterate on technical solutions.
• Bridge analytics and infrastructure by aligning business objectives with the chosen approach to deliver actionable insights.
• Develop production-grade ML/AI applications using tools like Streamlit and Gradio to deliver enterprise-wide data insights for informed decisions.
• Design and maintain cloud-based infrastructure on AWS and Databricks to support scalable analytics workflows.
• Create CI/CD pipelines and infrastructure-as-code using Terraform and AWS CloudFormation, adopting MLOps practices to boost productivity.
• Improve workflows and promote software engineering best practices such as version control, modular design, and testing to enhance efficiency and code quality.
• Maintain up-to-date knowledge of cloud technologies, MLOps trends, and application frameworks to identify opportunities for improvement.

Requirements

• PhD with at least 4 years of relevant professional experience.
• Master’s degree with 6+ years of relevant professional experience.
• Bachelor’s degree with 8+ years of relevant professional experience.
• Strong proficiency in Python, SQL, and Git.
• Experience with rapid application development frameworks such as Streamlit, Gradio, Starlette, or Next.js.
• Knowledge of DevOps or MLOps concepts and their application in data science workflows.
• Strong understanding of containerization with Docker or Podman.
• Ability to collaborate with data teams to support analytics workflows and generate insights.
• Proven problem-solving and critical-thinking skills for complex technical challenges.
• Excellent communication skills with experience engaging non-technical stakeholders.

Technologies

• Python
• SQL
• Git
• Streamlit
• Gradio
• Starlette
• Next.js
• AWS
• Databricks
• Terraform
• AWS CloudFormation
• Docker
• Podman
• PySpark
• AWS Step Functions
• Apache Airflow

Benefits

• Health insurance coverage
• Life and disability insurance
• Savings plan
• Company paid holidays
• Paid time off (PTO) for vacation and/or personal business
• Overtime eligibility
• Shift differential
• Discretionary bonus in addition to base pay
• Annual bonuses
• Long Term Incentives

What makes you successful in this role

• Balance speed with quality, delivering practical, working solutions when appropriate rather than pursuing perfection.
• High agency, proactively gathering information, identifying blockers, navigating ambiguity, and making thoughtful decisions with limited data.
• Technical versatility, comfortable moving between infrastructure work and data analysis as project needs shift.
• Bridge-builder capability, translating needs between data scientists, engineers, and business stakeholders to enable collaboration across domains.

Work arrangement

• Hybrid or remote position with most team members based in the Northern Virginia area; in-person collaboration is welcome but work is primarily remote and flexible.
• Standard schedule is a 9/80 arrangement, allowing a nine-hour day Monday through Thursday with every other Friday off.

Compensation

• USD 142,200 - 213,200 per year