Associate Data Scientist, DIBI

Northeastern University’s Defense Industrial Base Institute (DIBI) supports applied research that turns data science and AI into practical decision tools for defense sponsors. In this onsite role in Boston, MA, you will help develop, validate, and maintain datasets and analytical products that support the Navigator methodology, partnering across research, engineering, and sponsor-facing work.

What you’ll gain

• Comprehensive benefits for benefit-eligible employees, including medical, vision, dental, paid time off, tuition assistance, wellness and life, and retirement
• Multiple retirement plan options with extremely generous matching
• Tuition waiver for classes and advanced degree programs
• Commuting and transportation support

Responsibilities

You will contribute to research and operational scaling of Navigator methodology applied to defense contracts (SIB). A meaningful portion of the role focuses on development work, from prototype modeling and simulation through experimentation and design, as well as applying validation practices to keep analytical outputs reliable.

• Support research and development to scale the Navigator methodology for defense contracts, including development and implementation, prototype modeling and simulation, design, and experimentation (40%)
• Test and validate AI/ML tools for sprints, tasks, projects, and deliverables, including suggesting tools and systems that improve efficiency and better support human workflows (40%)
• Provide subject matter expertise across diverse application areas and ad hoc sponsor requests, including presenting information and answering questions from government sponsors and stakeholders
• Work with PI or co-PIs on project scoping and budgets as appropriate (systems and tools) (10%)
• Complete special projects assigned in collaboration with core leadership to scope proposals and opportunities (10%)
• Collaborate with the DIBI leadership team and Sr. Director, Strategy and Communications on recommendations for internal systems build-out (10%)

Requirements

• Master’s degree in Data Science, Statistics, Computer Science, Applied Mathematics, or a closely related field
• 2 to 4 years of professional experience in software engineering, data science, or applied R&D, with exposure to machine learning and AI system development in research, prototype, or production environments
• Advanced proficiency in Python and/or R studio and familiarity with modern ML/AI development workflows
• Exposure to C++ and/or Java for performance-critical components
• Experience contributing to the design, implementation, testing, or evaluation of ML/AI-enabled or simulation-driven software systems
• Demonstrated experience with data visualization and reporting tools (e.g., Tableau, Power BI, matplotlib, Plotly), including dashboards for executive or sponsor audiences
• Proven experience independently building, validating, and interpreting statistical or machine learning models, and communicating tradeoffs to non-technical stakeholders
• Strong command of data cleaning, ETL, and data quality assurance, including for messy or incomplete datasets
• Solid software engineering fundamentals, including version control (e.g., Git/GitHub), documentation, and reproducibility
• Excellent written and verbal communication, including presenting findings to leadership or sponsors
• U.S. Citizenship with the ability to obtain and maintain a security clearance

Technologies you may use

Python, R studio, C++, Java, Tableau, Power BI, matplotlib, Plotly, Git, GitHub, ETL, Azure, AWS, GovCloud, PostgreSQL, PostGIS, Svelte, React

Nice to have

• Experience contributing to government, defense, or security-related R&D programs (internships, fellowships, or full-time roles)
• Familiarity with simulation-based models (physics-based, network-based, agent-based, or stochastic simulations)
• Exposure to RAG, vector databases, embedding pipelines, or LLM-enabled systems
• Interest in UI development for technical or analyst-facing tools (e.g., Svelte, React)
• Familiarity with MLOps, experiment tracking, or reproducible research pipelines
• Exposure to graph-based ML or GNNs
• Monte Carlo or stochastic simulation methods
• Experience with synthetic data generation or simulation-in-the-loop workflows
• Familiarity with geospatial data and spatiotemporal datasets
• Experience working across multidisciplinary teams across research, engineering, and applied R&D environments

Position type: Research
Expected hiring range: $76,335.00 - $107,823.75
Compensation grade/pay type: 110S