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About This Role
Are you VIGILANT about your career? RealmOne definitely is!
RealmOne was built on the principle that people matter first and foremost. We believe in providing a strong work/life balance by investing in our employees and encouraging professional and personal growth. We do this by offering exceptional benefits, flexible schedules, and the tools necessary to achieve success through paid training, mentoring, and the opportunity to work alongside top-notch industry professionals.
Join us on this journey as we execute this mission-critical contract providing high-end analytics and data science services within the REALM of cybersecurity.
Your effort and expertise are crucial to the success and execution of this impactful mission. This opportunity supports a team of Data Scientists, Cryptologic Computer Scientists, Cryptanalytic Computer Scientists, Cryptologic Cyber Planners, Intrusion Analysts, Protocol Analysts, Signals Analysts and Reverse Engineers, responsible for improving, protecting, and defending our Nation’s Security.
Job Description:
- We are seeking a Data Scientist who is familiar with Agentic AI, RAG, and Machine Learning model training in addition to the customer’s enterprise data processing pipeline. You will be working to integrate AI into malware detection and malware analysis capabilities.
The Level 4 Data Scientist shall possess the following capabilities:
- Foundations: (Mathematical, Computational, Statistical).
- Data Processing: (Data management and curation, data description and visualization, workflow and reproducibility).
- Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations).
- Devise strategies for extracting meaning and value from large datasets. Make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application specific knowledge. Through analytic modeling, statistical analysis, programming, and/or another appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique features and limitations inherent in DOD data holdings. Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others with drawing appropriate conclusions from the analysis of such data. Effectively communicate complex technical information to non-technical audiences. Make informed recommendations regarding competing technical solutions by maintaining awareness of the constantly shifting DOD collection, processing, storage and analytic capabilities and limitations.
Qualifications:
- Bachelor’s Degree with 15 years of relevant experience.
- Associates degree with 17 years of relevant experience.
- Bachelor’s Degree must be in Mathematics, Applied Mathematics Statistics, Applied Statistics, Machine learning, Data Science, Operations Research, or Computer Science or a degree in a related field (Computer Information Systems, Engineering), a degree in the physical/hard sciences (e.g. physics, chemistry, biology, astronomy), or other science disciplines with a substantial computational component (i.e. behavioral, social, or life) may be considered if it included a concentration of coursework (5 or more courses) in advanced Mathematics (typically 300 level or higher, such as linear algebra, probability and statistics, machine learning) and/or computer science (e.g. algorithms, programming, , data structures, data mining, artificial intelligence). College-level requirement, or upper-level math courses designated as elementary or basic do not count. Note: A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university.
- Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least one high-level language, e.g. Python)), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering. Experience in more than one area is strongly preferred.
Position requires active Security Clearance with appropriate Polygraph
Pay Range: 212,000-267,000
The RealmOne pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Our approach to crafting offers considers various factors to establish an equitable and competitive compensation package. These considerations include, but are not limited to, the extent and intricacy of the role’s responsibilities, the candidate’s educational background, their work experience, and the specific competencies crucial for success in the role.
RealmOne Benefits:
- Healthcare Coverage + Insurance: Medical: Three (3) rich healthcare options through CareFirst with 100% or majority company-paid premiums. Tax-advantaged health savings account available with generous employer contribution. Dental + Vision: 100% employer-paid for employees and family, with a buy-up option available.
- Retirement + Savings: 401K – 10% TOTAL CONTRIBUTION – 5% safe harbor – 5% annual profit share (both immediately vested!).
- Paid Time Off + More: 4 weeks starting PTO – 11 federal holidays + 2 floating holidays – Paid hours for company-required training.
- Career Growth + Development: Access to FREE 24/7 learning via Udemy – Opportunities to participate in tech councils, industry initiatives, etc. – $7,500 annual Educational & Professional Development Assistance.
- MORE BENEFITS…FOR EVERY LIFESTYLE! – Paid parental leave – Annual swag drops – Flexible work schedules -Generous referral bonus program – Employee appreciation + family-friendly corporate events …and much more.
ABOUT US
- RealmOne is a mid-sized science and technology company dedicated to solving our customers’ toughest mission challenges.
- Headquartered in Columbia, MD., RealmOne supplies advanced cybersecurity, data science, and software engineering services and products to customers in the Government and commercial sectors.
- RealmOne delivers encompassing mission assurance and critical systems support to government customers across various U.S. locations to include Colorado, Georgia, Hawaii, Texas, Utah, and Virginia.
- RealmOne has won numerous awards, including Top Workplaces by the Baltimore Sun. Across more than 20 prime contracts, RealmOne is a premier innovator for the Government and Department of Defense, and our team is located across the United States.
Salary Context
This $212K-$267K range is above the 75th percentile 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 RealmOne, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($239K) sits 26% above the category median. Disclosed range: $212K to $267K.
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.
RealmOne AI Hiring
RealmOne has 1 open AI role right now. They're hiring across Data Scientist. Based in MD, US. Compensation range: $267K - $267K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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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