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About This Role
About the Team
STR's Analytics \& C2 (AC2\) Division researches and develops novel technologies to solve challenging national security problems through advanced analytics and C2 technologies. Our team consists of passionate and motivated engineers and scientists with advanced degrees in engineering, computer science, mathematics, physics, and data science. We use our expertise and creativity to take innovative ideas from conception to mature implementation to improve mission success of our customers.
The Signals Exploitation and Tracking (SET) Group in the Analytics and C2 Division focuses on applying machine learning, statistics, estimation theory, and information theory algorithms for signals exploitation, target tracking, predictive analytics, and system resource management.
The Role
As a Senior Data Scientist at STR, you will help develop disruptive technologies focused on signals exploitation, estimation theory, system resource management, and systems analysis. You will contribute to data analysis, engineering software, and validation/verification of cutting\-edge algorithms for novel application domains and modalities. You will explore fascinating datasets, develop cutting\-edge algorithmic techniques, and solve high\-impact, unique problems for our customers. In addition, you will participate on project teams and interact with customers. We are looking for someone to join our team onsite as this position is based in the Woburn, MA office.
Who you are:
- Active Top Secret Clearance Required with SCI eligibility, for which U.S citizenship is needed by the U.S government
- Have experience in intelligence or military\-related mission areas
- MS degree or a BS degree with at least 2 years of experience, in a scientific field such as applied math, physics, electrical engineering, computer science, or data science
- Experience building data analysis pipelines using standard tools (e.g., Python, C\+\+), including implementing software to identify key metrics for various data sets
- Experience designing and implementing experiments to verify and compare algorithm performance with measured data and/or simulations using standard analysis tools (e.g., Python)
- Experience with standard data science tools such as scikit\-learn, Pandas, and Matplotlib
- Proficiency in one or more programming languages: Python, C/C\+\+
- Able to work and collaborate on multi\-disciplinary teams
- Able to communicate technical foundations findings to technical and non\-technical audiences
Even better:
- Have experience in statistical inference, systems simulation capabilities, Monte Carlo or optimization methods, and/or data structures
- Have expertise working with time\-series data (sampled signals and/or discrete detection series)
- Have expertise working with geospatial and/or spatio\-temporal data
Pay Information
Full\-Time Salary Range: $110,000 \- $138,000
*The salary range listed is based on external market data. Offers are based on factors, such as but not limited to, the candidate's experience, education, training, key skills/critical skills, security clearances, and prevailing market and business conditions.*
STR is a growing technology company with locations near Boston, MA, Arlington, VA, near Dayton, OH, Melbourne, FL, and Carlsbad, CA. We specialize in advanced research and development for defense, intelligence, and national security in: cyber; next generation sensors, radar, sonar, communications, and electronic warfare; and artificial intelligence algorithms and analytics to make sense of the complexity that is exploding around us.
STR is committed to creating a collaborative learning environment that supports deep technical understanding and recognizes the contributions and achievements of all team members. Our work is challenging, and we go home at night knowing that we pushed the envelope of technology and made the world safer.
STR is not just any company. Our people, culture, and attitude along with their unique set of skills, experiences, and perspectives put us on a trajectory to change the world. We can't do it alone, though \- we need fellow trailblazers. If you are one, join our team and help to keep our society safe! Visit us at www.str.us for more info.
*STR is an equal opportunity employer. We are fully dedicated to hiring the most qualified candidate regardless of race, color, religion, sex (including gender identity, sexual orientation and pregnancy), marital status, national origin, age, veteran status, disability, genetic information or any other characteristic protected by federal, state or local laws.*
*If you need a reasonable accommodation for any portion of the employment process, email us at* *[email protected]* *and provide your contact info.*
*Pursuant to applicable federal law and regulations, positions at STR require employees to obtain national security clearances and satisfy the requirements for compliance with export control and other applicable laws.*
Salary Context
This $110K-$138K range is in the lower quartile for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At STR, 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 $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($124K) sits 37% below the category median. Disclosed range: $110K to $138K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
STR AI Hiring
STR has 1 open AI role right now. They're hiring across Data Scientist. Based in Woburn, MA, US. Compensation range: $138K - $138K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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