Sr. Principal Data Scientist – Machine Learning Engineer

RELOCATION ASSISTANCE: No relocation assistance available

CLEARANCE TYPE: None

TRAVEL: Yes, 10% of the Time

Description

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.

At Northrop Grumman, the Insights & Intelligence (i2) organization seeks to embed trusted AI and data insights into every business decision at the company. Our Applied Data Science & AI team builds lightweight, production‑grade analytics solutions that solve problems traditional enterprise tools struggle to meet.

Our team operates with high autonomy, working closely with engineers and business leaders to identify high‑value problems, build apps and other products from the ground up, and deploy them into production. We value speed, intellectual curiosity, and the ability to toggle between “prototype rapidly” and “engineer for production” based on what the situation demands. 

As a data scientist / machine learning engineer, you will be a technical force multiplier—working with program teams to understand their challenges, building the infrastructure and applications to address them, and deploying production solutions that drive high-impact decisions.

Job duties include, but are not limited to:

• Work directly with stakeholders (engineers, program managers, subject matter experts) to scope problems, identify constraints, and iterate on technical solutions 

• Bridge analytics and infrastructure by understanding both the business problem and the approach, then building systems that deliver insights

• Build user‑friendly, production‑grade ML/AI applications (e.g., Streamlit, Gradio) that provide data insights to teams across the enterprise and enable better decision making  

• Develop and maintain cloud‑based infrastructure (AWS, Databricks) and tooling to support scalable and reliable data analytics workflows 

• Design and implement CI/CD pipelines, infrastructure‑as‑code (Terraform, AWS CloudFormation), and MLOps practices that enhance team productivity 

• Optimize existing workflows and advocate for software engineering best practices (version control, modular design, testing) to drive team efficiency and code quality   

• Stay current on cloud technologies, MLOps trends, and application frameworks to identify opportunities for improvement 

What Makes You Successful in this role:

You balance speed with quality: You can assess when “good enough now” beats “perfect later” and prioritize impact and working solutions over perfection. 

You have high agency: You proactively gather information, identify blockers, can operate in ambiguity, and make thoughtful decisions with incomplete information. 

You’re technically versatile: You’re comfortable diving into infrastructure one day and analyzing a dataset the next, stepping into different roles depending on project needs. 

You’re a bridge‑builder: You can talk to data scientists about model deployment, engineers about infrastructure, and business stakeholders about their problem. You translate and collaborate across domains. 

Work Arrangement

This is a hybrid/remote position. Most of our team is based in the Northern Virginia area, and we welcome candidates who can sometimes collaborate in person, but we operate primarily remotely and value flexibility. This position’s standard work schedule is a 9/80. The 9/80 schedule allows employees who work a nine-hour day Monday through Thursday to take every other Friday off.

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 Bachelor’s degree with 8 years of relevant professional experience 

• Must have strong proficiency with Python, SQL, and Git 

• Must have experience with frameworks for rapid application development (e.g., Streamlit, Gradio, Starlette, Next.js) 

• Must have knowledge of DevOps or MLOps concepts and their application in data science workflows 

• Must have strong understanding of containerization (e.g., Docker, Podman) 

• Must have the ability to work collaboratively with data teams (data scientists, analysts) to support analytics workflows and insights 

• Must have demonstrated problem‑solving and critical‑thinking skills with an ability to handle complex technical challenges 

• Must have excellent communication skills and comfort engaging with non‑technical stakeholders 

Preferred Qualifications:

• Proven track record of deploying and monitoring production‑grade software systems on AWS 

• Experience with Databricks and PySpark for data transformation and analytics 

• Proven experience building and deploying web‑based visualization or decision‑support tools for business use cases 

• Exposure to workflow orchestration tools (e.g., AWS Step Functions, Apache Airflow) and infrastructure‑as‑code tools (e.g., Terraform, AWS CloudFormation) 

• Familiarity with scalable data architectures and machine learning deployment frameworks 

• Domain experience in manufacturing analytics, operations research, supply chain optimization, or financial forecasting 

• Background in consulting, forward‑deployed engineering, or other client‑facing technical roles where you translated ambiguous business problems into technical solutions 

Primary Level Salary Range: $142,200.00 - $213,200.00

The above salary range represents a general guideline; however, Northrop Grumman considers a number of factors when determining base salary offers such as the scope and responsibilities of the position and the candidate's experience, education, skills and current market conditions.

Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay. Annual bonuses are designed to reward individual contributions as well as allow employees to share in company results. Employees in Vice President or Director positions may be eligible for Long Term Incentives. In addition, Northrop Grumman provides a variety of benefits including health insurance coverage, life and disability insurance, savings plan, Company paid holidays and paid time off (PTO) for vacation and/or personal business.

The application period for the job is estimated to be 20 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates.

Northrop Grumman is an Equal Opportunity Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other protected class. For our complete EEO and pay transparency statement, please visit http://www.northropgrumman.com/EEO. U.S. Citizenship is required for all positions with a government clearance and certain other restricted positions.