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About This Role
Vantor is forging the new frontier of spatial intelligence, helping decision makers and operators navigate what’s happening now and shape what’s coming next. Vantor is a place for problem solvers, changemakers, and go\-getters—where people are working together to help our customers see the world differently, and in doing so, be seen differently. Come be part of a mission, not just a job, where you can: Shape your own future, build the next big thing, and change the world.
To be eligible for this position, you must be a U.S. Citizen. This position requires an active U.S. Government security clearance, applicants who do not currently hold the required clearance will not be eligible for consideration. Employment for cleared roles is contingent upon verification of clearance status.
*Export Control/ITAR:* Certain roles may be subject to U.S. export control laws, requiring U.S. person status as defined by 8 U.S.C. 1324b(a)(3\).
Please review the job details below.
This position requires an active U.S. Government Security Clearance at the TS/SCI level with CI polygraph.
Project: The team will design, develop, validate, and demonstrate an integrated analytical capability supporting pattern recognition, anomaly detection, predictive analytics, agent\-based modeling (ABM) or equivalent, and decision support in a Ubiquitous Technical Surveillance (UTS) environment. The effort will enhance Joint Force survivability by enhancing the characterization of the UTS threat and providing a mission\-agnostic risk\-management tool for commanders and operators to utilize across the spectrum of conflict. The enterprise will leverage this analysis and these tools to increase isolated personnel (IP) survivability by enhancing evasion training, planning, and execution, while bolstering force protection for PR task forces.
TheData Scientistwill be part of a cross\-functional, highly technical team addressing difficult analytical problems supporting a high\-value, national defense mission. Success depends on the development of accurate, scalable, and mission\-tailored data solutions that enable timely and informed decision\-making. The work directly impacts national security objectives by transforming complex data into actionable intelligence in a highly specialized and sensitive domain. A strong mission focus is essential, and experience with the Personnel Recovery mission is a plus.
Responsibilities:
- Data preparation
- Feature engineering
- Statistical modeling
- Predictive analytics
- Deep learning
- Time\-series analysis
- Model validation
- Explainable AI
- Performance benchmarking
- Technical documentation
- Automate and maintain data extraction, cleaning, processing, and analysis workflows using Python, SQL, and ETL tools under established best practices.
- Process and analyze structured and unstructured datasets using big data or cloud\-native frameworks (e.g., Spark, Hadoop, or managed cloud services).
- Develop, test, and evaluate predictive models and statistical analyses to support mission\-focused use cases.
- Contribute to end\-to\-end data science and data engineering workflows, from data preparation and feature engineering through model development and results delivery, with guidance from senior team members as needed.
- Write clear, well\-structured documentation of methods, assumptions, and results; assist with briefings or presentations to technical and non\-technical stakeholders.
- Support the use of Large Language Models (LLMs) within existing systems and workflows.
- Collaborate closely with data scientists, engineers, analysts, and mission partners to refine requirements and iterate on solutions.
Minimum Qualifications:
- US Citizen and must have an active TS/SCI clearance, with ability to obtain CI Poly.
- 6 years of professional experience in data science, analytics, or data engineering roles.
- Bachelor’s degree in data science, computer science, engineering, statistics, GIS, or related discipline. Degree may be substituted with an additional 4 years of experience.
- Strong coding proficiency in Python and SQL.
- Hands\-on experience with data manipulation, feature engineering, machine learning libraries (e.g., scikit\-learn, PyTorch, TensorFlow), and automation tools.
- Experience contributing to full\-cycle data projects, including data preparation, modeling, validation, and reporting.
- Ability to clearly communicate technical concepts and analytical results in writing and verbally.
- Ability to work effectively in a collaborative environment, taking direction and feedback while owning assigned technical tasks.
- Must be able to work on\-site in Herndon, Va, as required.
Preferred Qualifications:
- Master’s degree in data science, computer science, statistics, engineering, or a related technical field.
- A strong background in statistics and graph analytics.
Pay Transparency: To support pay transparency, Vantor includes salary ranges in all U.S. job postings. Starting pay for this role will fall within the listed range and will be based on factors such as experience, qualifications, skills, location, and market conditions. Candidates who meet the minimum requirements for the role should not expect to receive compensation at the top of the range. The listed range reflects the expected pay for this position, and final offers will be determined based on each candidate’s experience, expertise, and alignment with the role.
- The base pay for this position within the Washington, DC metropolitan area is: $116,000\.00 \- $154,000\.00 \- $169,400\.00 annually.
For all other states, we use geographic cost of labor as an input to develop market\-driven ranges for our roles, and as such, each location where we hire may have a different range.
Benefits: Vantor offers a competitive total rewards package that goes beyond the standard, including a robust 401(k) with company match, mental health resources, and unique perks like student loan repayment assistance, adoption reimbursement and pet insurance to support all aspects of your life. You can find more information on our benefits at: https://www.Vantor.com/careers
The application window is three days from the date the job is posted and will remain posted until a qualified candidate has been identified for hire. If the job is reposted regardless of reason, it will remain posted three days from the date the job is reposted and will remain reposted until a qualified candidate has been identified for hire.
The date of posting can be found on Vantor's Career page at the top of each job posting.
To apply, submit your application via Vantor's Career page.
EEO Policy: Vantor is an equal opportunity employer committed to an inclusive workplace. We believe in fostering an environment where all team members feel respected, valued, and encouraged to share their ideas. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender identity, sexual orientation, disability, protected veteran status, age, or any other characteristic protected by law.
Salary Context
This $116K-$169K range is below the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Vantor, 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 $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($142K) sits 26% below the category median. Disclosed range: $116K to $169K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Vantor AI Hiring
Vantor has 4 open AI roles right now. They're hiring across Data Scientist, Research Scientist, AI/ML Engineer. Positions span Herndon, VA, US, Westminster, CO, US. Compensation range: $169K - $215K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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