Posted:
2/17/2026, 5:03:14 AM
Location(s):
Florida, United States ⋅ Virginia, United States ⋅ Falls Church, Virginia, United States
Experience Level(s):
Expert or higher ⋅ Senior
Field(s):
Data & Analytics
At Northrop Grumman, our employees have incredible opportunities to work on revolutionary systems that impact people's lives around the world today, and for generations to come. Our pioneering and inventive spirit has enabled us to be at the forefront of many technological advancements in our nation's history - from the first flight across the Atlantic Ocean, to stealth bombers, to landing on the moon. We look for people who have bold new ideas, courage and a pioneering spirit to join forces to invent the future and have fun along the way. Our culture thrives on intellectual curiosity, cognitive diversity and bringing your whole self to work — and we have an insatiable drive to do what others think is impossible. Our employees are not only part of history, they're making history.
The Insights & Intelligence (i2) Supplier AI & Analytics team operates within the CIDO organization and drives AI, analytics, and data-driven decision making within Global Supply Chain. The team is responsible for executing Global Supply Chain’s analytics and AI strategy by translating leadership priorities into scalable, production-grade solutions that strengthen supplier performance, reduce risk, and improve enterprise visibility.
We work closely with the Supply Chain Leadership Council (SCLC) to prioritize high-impact use cases and deliver durable analytics and AI capabilities. Our focus is on turning supplier data into insight through sound analytical methods, statistical rigor, and engineered solutions that can be governed, reused, and scaled across the enterprise.
In this Sr. Principal Data Scientist role, you will serve as a senior technical leader and force multiplier. You will help shape how supplier data is analyzed, modeled, and operationalized, and you will design and deploy production systems that enable better supply chain decisions at scale.
What You’ll Do:
Partner with Global Supply Chain leadership and subject-matter experts to define analytical problem statements, clarify assumptions and constraints, and determine appropriate statistical or modeling approaches
Perform exploratory data analysis and statistical analysis to identify patterns, drivers, and risks in supplier performance and related supply chain data
Design, develop, and deploy machine learning and advanced analytics solutions that support supplier performance, risk identification, and decision-making
Translate analytical outputs into user-facing, production-grade ML and AI applications (for example, Streamlit or Gradio tools) that make insights accessible to non-technical users
Bridge analytics and infrastructure by building systems that operationalize models and analyses through scalable data pipelines and applications
Develop and maintain cloud-based infrastructure and tooling using platforms such as AWS and Databricks to support reliable analytics workflows
Design and implement CI/CD pipelines, infrastructure-as-code, and MLOps practices that support model deployment, monitoring, and iteration
Improve existing workflows and advocate for strong software engineering and analytical best practices, including version control, testing, documentation, and reproducibility
Stay current on statistical methods, machine learning techniques, and analytics tooling, applying them where they deliver measurable business value
What Makes You Successful Here
You apply statistical rigor with business judgment.
You know how to explore data, select appropriate methods, and communicate uncertainty without overcomplicating the message.
You balance speed and quality.
You understand when fast analysis is sufficient and when deeper modeling or engineering is required.
You operate with high agency.
You proactively identify analytical opportunities, data limitations, and risks, and move work forward with minimal direction.
You are technically versatile.
You are comfortable moving between data analysis, modeling, application development, and infrastructure depending on the problem.
You are a bridge-builder.
You translate between data scientists, engineers, and supply chain leaders, helping align analysis, systems, and decisions.
Basic Qualifications
Must have a PhD with 4 years of relevant professional experience, OR a Master’s degree with 6 years of relevant professional experience, OR a Bachelor’s degree with 8 years of relevant professional experience
Must have a strong proficiency in Python, SQL, and Git
Must have a solid foundation in statistical analysis, data exploration, and applied modeling
Must have experience building and deploying machine learning or advanced analytics solutions
Must have experience with rapid application development frameworks such as Streamlit, Gradio, Starlette, or Next.js
Must have working knowledge of DevOps or MLOps concepts as applied to data science workflows
Must have experience with containerization technologies such as Docker or Podman
Must have the ability to collaborate effectively with data scientists and analysts on analytical methods and results
Strong problem-solving and critical-thinking skills
Clear communication skills and comfort engaging with non-technical stakeholders
Preferred Qualifications
Demonstrated experience deploying, operating, and monitoring production analytics or ML systems on AWS
Experience with Databricks and PySpark for data transformation, feature engineering, and analytics
Experience building and deploying analytical or decision-support tools for business users
Familiarity with workflow orchestration tools such as AWS Step Functions or Apache Airflow
Experience with infrastructure-as-code tools including Terraform or AWS CloudFormation
Understanding of scalable data architectures and model lifecycle management
Domain experience in supply chain analytics, manufacturing analytics, operations research, or financial forecasting
Background in consulting or other client-facing technical roles where statistical analysis and modeling were used to inform decisions under ambiguity
Website: https://northropgrumman.com/
Headquarter Location: Falls Church, Virginia, United States
Employee Count: 10001+
Year Founded: 1994
IPO Status: Public
Last Funding Type: Grant
Industries: Data Integration ⋅ Manufacturing ⋅ Remote Sensing ⋅ Security ⋅ Software