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About This Role
Position Summary
Prometheus Federal Services (PFS) is a trusted partner of federal health agencies. We are seeking a Senior Data Scientist to support Department of Veterans Affairs (VA) programs through advanced analytics, predictive modeling, data science, and AI-enabled solutions. This role will focus on transforming complex VA data assets into actionable insights that support operational decision-making, healthcare outcomes, data modernization initiatives, and enterprise reporting.
The Senior Data Scientist will collaborate with business stakeholders, data engineers, analysts, and program leadership to design, develop, and operationalize advanced analytical solutions. The ideal candidate brings deep expertise in statistical analysis, machine learning, data engineering concepts, and healthcare analytics, along with experience working within complex federal data environments.
Reasonable Accommodations Statement
To succeed in this job, an individual must be able to satisfactorily perform each essential duty. Reasonable Accommodations may be made to enable qualified individuals with disabilities to perform essential functions.
Essential Duties and Responsibilities
Data Science, Advanced Analytics & AI/ML* Develop and implement advanced analytical models to identify trends, patterns, risks, and opportunities within large and complex datasets.
- Design, build, and deploy machine learning models to support predictive and prescriptive decision-making.
- Apply statistical techniques and data science methodologies to solve complex business and operational challenges.
- Develop forecasting, classification, clustering, anomaly detection, and optimization models to support program objectives.
- Support AI-enabled analytical solutions that improve operational insight, performance measurement, and resource planning.
- Evaluate emerging AI, machine learning, and advanced analytics technologies for applicability within VA environments.
- Develop model monitoring and evaluation frameworks to ensure accuracy, stability, explainability, and performance.
Data Exploration, Preparation & Feature Engineering* Conduct exploratory data analysis (EDA) to uncover patterns, anomalies, and key business drivers.
- Perform data profiling and quality assessments to identify issues impacting analytical outcomes.
- Develop feature engineering strategies to improve model performance and business relevance.
- Prepare and transform structured and semi-structured datasets for advanced analytical applications.
- Collaborate with data engineering teams to establish scalable analytical datasets and model-ready data pipelines.
Data Integration, Engineering & Architecture Collaboration* Partner with data engineers and architects to integrate data across diverse VA systems, databases, APIs, and enterprise platforms.
- Support the design and optimization of data pipelines and analytical workflows.
- Contribute to data modeling efforts that improve accessibility, scalability, and performance of analytical solutions.
- Assist in developing reusable analytical frameworks and data science assets across programs.
- Ensure analytical solutions align with data governance, security, and compliance requirements.
Business Partnership, Strategy & Communication* Translate business questions, policy objectives, and operational needs into analytical approaches and measurable outcomes.
- Present analytical findings, model outputs, and recommendations to technical and non-technical stakeholders.
- Develop executive-level briefings, data visualizations, and decision-support materials.
- Partner with program leadership to identify opportunities to transition from descriptive reporting to predictive and prescriptive analytics.
- Communicate model assumptions, limitations, and risks to stakeholders in a clear and understandable manner.
Governance, Documentation & Continuous Improvement* Develop and maintain documentation for analytical methods, models, data sources, assumptions, and validation procedures.
- Support analytical governance and best practices related to model lifecycle management and reproducibility.
- Participate in peer reviews and quality assurance activities for analytical products.
- Continuously evaluate new methodologies and technologies to improve analytical capabilities and program outcomes.
- Mentor junior data scientists, analysts, and technical team members.
Minimum Qualifications* Bachelor's degree in Data Science, Statistics, Computer Science, Applied Mathematics, Operations Research, Engineering, Healthcare Informatics, or a related field.
- 8+ years of experience in data science, advanced analytics, machine learning, or related technical roles.
- Strong proficiency in Python for data science and machine learning applications (e.g., pandas, NumPy, scikit-learn, TensorFlow, PyTorch, or similar).
- Advanced knowledge of statistical methods, predictive modeling, and machine learning techniques.
- Strong proficiency in SQL for data extraction, transformation, and analysis.
- Experience developing and validating machine learning models, including classification, regression, clustering, forecasting, and anomaly detection.
- Experience conducting exploratory data analysis and communicating insights to diverse audiences.
- Familiarity with model evaluation techniques, performance metrics, and validation methodologies.
- Experience working with large-scale structured and semi-structured datasets.
- Understanding of data engineering concepts, including ETL/ELT processes, data pipelines, and cloud-based data platforms.
- Strong problem-solving, critical thinking, and analytical skills.
- Excellent written and verbal communication skills.
- Authorized to work in the U.S. indefinitely without sponsorship.
- Ability to obtain a Public Trust clearance.
Preferred Qualification* Master's degree or PhD in Data Science, Statistics, Computer Science, Applied Mathematics, Operations Research, Engineering, Healthcare Informatics, or a related discipline.
- Experience supporting the Department of Veterans Affairs (VA), Veterans Health Administration (VHA), or other federal healthcare agencies.
- Experience working with healthcare, clinical, claims, operational, or population health datasets.
- Experience with cloud-based analytics platforms such as Azure Synapse, Azure Machine Learning, Databricks, AWS, or comparable environments.
- Familiarity with Power BI, Tableau, or other business intelligence and visualization platforms.
- Experience supporting enterprise data modernization, governance, or digital transformation initiatives.
- Experience developing explainable AI (XAI) and responsible AI solutions in regulated environments.
- Experience mentoring and leading technical teams or analytical workstreams.
Compensation & Benefits
PFS offers a benefits package that may include health, dental, and vision coverage; flexible spending accounts; disability and life insurance; a retirement plan; paid time off; and other programs to support employees and their families. Learn more about PFS Benefits.
The posted salary range represents PFS's good-faith estimate for this role. Actual compensation offered will be determined by a combination of factors, including but not limited to: the candidate's education, knowledge, skills, competencies, and experience;internal equity; geographic location; and contract and organizational requirements. PFS is committed to fair, consistent, and equitable compensation practices across the organization, and offers are calibrated to maintain internal pay equity while remaining competitive in the external market.
Salary Range: $100,000 - $140,000
Equal Employment Opportunity
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, parental status, national origin, age, disability, genetic information, political affiliation, military service, or other status protected by law.
Accommodations
PFS is committed to providing equal employment opportunities to all applicants. If you require a reasonable accommodation during the application or interview process, please contact us at [email protected]. Reasonable accommodations are available to ensure applicants with disabilities have equal access to the hiring process, in accordance with the Americans with Disabilities Act (ADA) and applicable laws.
Workplace Health, Safety, and Compliance
This position may be subject to client‑specific or government‑mandated vaccination, health, or safety requirements, which may change over time.
Texting Privacy Policy and Information:
- Message type: Informational; you will receive text messages regarding your application and potentially regarding interview scheduling.
- No mobile information will be shared with third parties/affiliates for marketing/promotional purposes.
- Message frequency will vary depending on the application process.
- Msg & data rates may apply.
- Opt out at any time by texting "stop".
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Salary Context
This $100K-$140K range is in the lower quartile for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Prometheus Federal Services, 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 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($120K) sits 37% below the category median. Disclosed range: $100K to $140K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Prometheus Federal Services AI Hiring
Prometheus Federal Services has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $140K - $140K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
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