At Northrop Grumman, the Insights & Intelligence (i2) organization seeks to embed trusted AI and data insights into every business decision at the company. We build 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 principal data scientist, you will lead analytical projects that drive high-impact business decisions—working with program teams to deeply understand their challenges, developing rigorous analytical approaches and models, and delivering insights through production-quality code, applications, and strategic recommendations.
Job duties include, but are not limited to:
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
Apply deep analytical thinking to decompose complex problems—critically evaluate data quality and relevance, challenge assumptions, and design methods that address the business need
Develop statistical models, machine learning solutions, and analytical frameworks that deliver actionable insights and drive operational decisions
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
Write production-quality Python code and develop analytical pipelines using cloud-based platforms (AWS, Databricks) to support scalable and reproducible data science workflows
Deliver insights through multiple channels—executive recommendations, analytical reports, interactive dashboards, and direct consultation with business leaders
Take ownership of the technical quality and business impact of your work—make thoughtful methodological decisions, validate approaches rigorously, and stand behind your recommendations
Stay current on analytical methods, statistical techniques, and domain-specific best practices to continuously improve the quality and impact of your work
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 think deeply about problems: You don't just apply standard methods—you critically evaluate whether your analytical approach addresses the business problem, challenge your assumptions, and dig into the data to understand what's really happening.
You're intellectually curious: You get excited about jumping into new domains, asking smart questions, and learning enough about unfamiliar business problems to design the right analytical approach.
You have high agency: You proactively gather information, identify blockers, can operate in ambiguity, and make thoughtful decisions with incomplete information.
You take ownership: You're accountable for both the technical quality and business impact of your work. When you deliver a recommendation, you stand behind the methodology and can defend your approach.
You're a bridge‑builder: You can talk to engineers about implementation, business stakeholders about their problems, and executives about strategic implications. 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:
Minimum of 5 years of hands-on experience in data science, data analysis, or other relevant professional experience
Must have strong proficiency with Python, SQL, and Git
Must have deep understanding of statistical methods, machine learning algorithms, and when to apply different analytical techniques
Must have experience developing and deploying machine learning models in production environments
Must have proven ability to translate complex business problems into rigorous analytical frameworks
Must have demonstrated problem‑solving and critical‑thinking skills with an ability to handle complex analytical challenges
Must have excellent communication skills and ability to deliver compelling, actionable recommendations to non‑technical stakeholders
Must have strong track record of ownership and accountability for technical decisions and project outcomes
Preferred Qualifications:
Experience with AWS and Databricks for data processing and model development
Experience with PySpark for large-scale data transformation and analytics
Proven experience building and deploying web‑based visualization or decision‑support tools for business use cases (e.g., Streamlit, Dash)
Knowledge of MLOps concepts and best practices for deploying models to production
Understanding of containerization (e.g., Docker) and cloud-based deployment
Familiarity with advanced analytical techniques such as causal inference, experimental design, time series forecasting, optimization, or Bayesian methods
Domain experience in program management, business management, operations research, earned value management, or financial forecasting
Background in consulting, forward‑deployed engineering, or other client‑facing technical roles where you translated ambiguous business problems into technical solutions