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About This Role
About the Opportunity
JOB SUMMARY
The Defense Industrial Base Institute at Northeastern University (NU) – a rapidly growing defense focused institute that conducts cutting-edge applied R&D – is seeking an experienced and highly capable Jr. Data Scientist with expertise in applying methods/frameworks/systems that apply ML&AI. The Jr. Data Scientist will operate as part of a multi-disciplinary team (academia, industry and government) and will contribute directly to the successful execution of federally funded projects/activities,
Responsibilities include providing technical expertise and analytical support to Principal investigators or co-principal investigators as a Jr. Data Scientist for a wide range of projects and activities involving machine learning (ML) and artificial intelligence (AI), including autonomy, sensing and communication, and decision support systems, among others. The Jr, Data Scientist will work collaboratively with and help guide, multi-disciplinary teams across the DIBI Higher Education Consortium — spanning academic, government, and industry partners — to design, validate, and maintain datasets and analytical products that inform institute leadership and sponsor decision-making. Specifically, the Jr. Data Scientist will take a role in building and maintaining the data pipelines and analytical components underlying the existing Navigator methodology, working closely with — and providing guidance to — data researchers, engineers, and leadership across the institute.
Successful candidates will be responsible team players and passionate about machine learning technologies, as well as possess a deep understanding of machine learning technology and experience in turning machine learning systems into practical, state-of-the-art systems that can be enhanced for projects or activities as per federal agency. A close working relationship with the team, along with creative thinking on the application of the Navigator framework for government and industry contracts, will be required.
The primary office for this position is located at NU’s Boston Campus Through NU, the institute offers an impressive benefits package, including multiple retirement plan options with extremely generous matching, as well as tuition waiver for classes and advanced degree programs. A full description of available benefits can be found on the NU website .
MINIMUM QUALIFICATIONS
Education & Experience
Required
- Master’s degree in Data Science, Statistics, Computer Science Computer Science, Applied Mathematics, or a closely related field
2–4 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. *
Preferred
- Master’s degree or Ph.D with a focus on data science, data ‑ intensive systems, network science, optimization, or related areas.
- Experience contributing to government, defense, or security ‑ related R&D programs (internships, fellowships, or full ‑ time roles).
- Familiarity with simulation ‑ based models (e.g., physics ‑ based, network ‑ based, agent ‑ based, or stochastic simulations) for analysis, experimentation, or decision support.
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Skills & Attributes
Required
- 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-drive software systems.
- Demonstrated experience with data visualization and reporting tools (e.g., Tableau, Power BI, matplotlib, Plotly), including designing 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 processes, and data quality assurance practices, including for messy or incomplete real-world datasets.
- Solid software engineering fundamentals, including version control (e.g., Git/GitHub), documentation, and reproducibility.
- Excellent written and verbal communication skills, with experience presenting analytical findings directly to leadership or sponsors.
U.S. Citizenship with the ability to obtain and maintain a security clearance *
Desired:
- Exposure to Retrieval‑Augmented Generation (RAG), vector databases, embedding pipelines, or LLM‑enabled systems.
- Familiarity with cloud computing environments (e.g., Azure, AWS or GovCloud equivalents).
- Introductory experience with graph‑based ML or GNNs is a plus
- Monte Carlo or stochastic simulation methods
- Synthetic data generation or simulation‑in‑the‑loop workflows
- Familiarity with geospatial data, spatiotemporal datasets, or PostgreSQL/PostGIS is a plus.
- Interest in UI or frontend development for technical or analyst‑facing tools (e.g., Svelte , React, or similar frameworks).
- Exposure to dashboarding or BI tools for analyst-facing or leadership-facing reporting
- Familiarity with MLOps concepts, experiment tracking, or reproducible research pipelines.
Experience working with multidisciplinary teams across research, engineering, and applied R&D environments. *
KEY RESPONSIBILITIES & ACCOUNTABILITIES
Support Research and Development efforts to scale Navigator methodology applied to defense contracts (SIB), to include, development and implementation, prototype modeling & simulation, design, and experimentation. This supports the defense funding received that supports this role (40%)
Test and validation of soAI/ML Tools for sprints, tasks, projects and deliverables; to include scaling of projects by suggesting tools and systems that enhance efficiency and support humans in their work (40%)
Provide subject matter expertise across a diverse set of application areas and ad hoc requests that may come in from the sponsor. Present information and answer questions form government sponsors and stakeholders.Research and support PI or CO-PIs with scoping project budgets as appropriate (Systems, tools) (10%)
Special Projects as assigned: Working closely with Managing Director, Founding Director and other core leadership members, help to scope proposals and opportunities. Working closely with DIBI Leadership Team and Sr. Director, Strategy and Communications to make recommendations on systems build out for internal operations (10%)
Position Type
Research Additional Information
Northeastern University considers factors such as candidate work experience, education and skills when extending an offer.
Northeastern has a comprehensive benefits package for benefit eligible employees. This includes medical, vision, dental, paid time off, tuition assistance, wellness & life, retirement- as well as commuting & transportation. Visit https://hr.northeastern.edu/benefits/ for more information.
All qualified applicants are encouraged to apply and will receive consideration for employment without regard to race, religion, color, national origin, age, sex, sexual orientation, disability status, or any other characteristic protected by applicable law.
Compensation Grade/Pay Type:
110S Expected Hiring Range:
$76,335.00 - $107,823.75 With the pay range(s) shown above, the starting salary will depend on several factors, which may include your education, experience, location, knowledge and expertise, and skills as well as a pay comparison to similarly-situated employees already in the role. Salary ranges are reviewed regularly and are subject to change.
Salary Context
This $76K-$107K range is in the lower quartile for Data Scientist roles in our dataset (median: $161K across 138 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Northeastern University, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000. This role's midpoint ($92K) sits 52% below the category median. Disclosed range: $76K to $107K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Northeastern University AI Hiring
Northeastern University has 1 open AI role right now. They're hiring across Data Scientist. Based in Boston, MA, US. Compensation range: $107K - $107K.
Location Context
AI roles in Boston pay a median of $215,000 across 360 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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