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About This Role
Essential Duties and Responsibilities:* Perform hands\-on data analysis and modeling with huge data sets.
- Apply data mining, NLP, and machine learning (both supervised and unsupervised) to improve relevance and personalization algorithms.
- Work side\-by\-side with product managers, software engineers, and designers in designing experiments and minimum viable products.
- Discover data sources, get access to them, import them, clean them up, and make them "model\-ready."
- Create and refine features from the underlying data.
- Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your optimizations and communicate results to peers and leaders.
- Explore new design or technology shifts in order to determine how they might connect with the customer benefits we wish to deliver.
- Collect, validate, clean, integrate, and analyze data from multiple sources, including the Human Performance Data Management System (HPDMS).
- Conduct exploratory, statistical, and predictive analyses to identify trends, correlations, risks, and opportunities related to human performance and mission readiness.
- Develop and maintain databases, spreadsheets, reports, and automated dashboards that communicate program performance and outcomes.
- Support survey design, administration, analysis, and interpretation to inform program improvements.
- Collaborate with program staff, biostatisticians, clinicians, and other cross\-functional partners to translate findings into actionable recommendations.
- Develop, document, and refine analytical methods, models, and tools that address complex operational and health\-related challenges.
- Present analytical findings clearly to technical and nontechnical stakeholders while protecting sensitive information.
- Perform other duties as assigned.
Minimum Requirements
- Bachelor's degree in relevant field of study and 3\+ years of relevant professional experience required, or equivalent combination of education and experience.
- Advanced degree in Computer Science, Information Systems, Business Analytics, Mathematics, Statistics, Engineering, Business Administration or a related field preferred.
- 1\-3\+ years of professional experience with applying quantitative research in optimizing human decisions using technologies like machine learning and/or deep learning.
- 1\+ years using major machine learning/deep learning frameworks (e.g., Scikit\-learn, PyTorch, TensorFlow and Keras) and algorithms (e.g., CNN, GAN, LSTM, RNN, XGBOOST).
- 1\+ years of data engineering experience with modern big data analytics architectures (Hadoop, SQL, HIVE, Spark, Snowflake, etc.) on major cloud platforms (e.g., AWS, Azure, Google Cloud).
- 1\+ years programming skill in Python, Scala, or Julia.
- Working knowledge with modern cloud\-based data storage and compute environments (e.g., AWS Sagemaker, Databricks in Azure, AI\-platform in GCP, etc.).
- Experience deploying ML models into discovery/production environment to drive insights using MLOps a plus.
- Experience working in an agile delivery model a plus.
- Demonstrated leadership and self\-direction. Willingness to both teach others and learn new techniques.
- Ability to communicate complex ideas in a clear, precise, and actionable manner.
- Excellent communication and presentation skills, with the ability to articulate new ideas and concepts to technical and non\-technical partners.
- PowerBI experience a plus.
Job Specific Requirements:
- Bachelor's degree in data science, statistics, mathematics, computer science, or a related field.
- Demonstrated experience using statistical analysis tools, database systems, and data visualization platforms.
- Experience preparing, analyzing, and interpreting complex datasets.
- Ability to communicate technical findings to diverse audiences.
- Security: Requires anactive Secret security clearanceand U.S. citizenship.
- Survey Design \& Analysis: Assist in designing surveys, collecting, and analyzing survey data to inform program improvements Collaboration: Work with cross\-functional teams, including biostatisticians, to develop data\-driven recommendations for SOF and family support initiatives.
- Technical Solutions: Develop and refine analytical methods and tools to address complex operational and health\-related challenges.
- Experience supporting government, defense, military health, or human performance programs.
- Experience developing automated dashboards, predictive models, or decision\-support tools.
- Knowledge of data governance, privacy, security, and quality\-control practices.
- Familiarity with multidisciplinary health, performance, or readiness data.
- Skills: Proficiency in data analysis tools, statistical software, database systems, and visualization platforms.
- Experience: Experience with government or defense\-related data projects is preferred.
EEO Statement
Maximus is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, age, national origin, disability, veteran status, genetic information and other legally protected characteristics.
Pay Transparency
Maximus compensation is based on various factors including but not limited to job location, a candidate's education, training, experience, expected quality and quantity of work, required travel (if any), external market and internal value analysis including seniority and merit systems, as well as internal pay alignment. Annual salary is just one component of Maximus's total compensation package. Other rewards may include short\- and long\-term incentives as well as program\-specific awards. Additionally, Maximus provides a variety of benefits to employees, including health insurance coverage, life and disability insurance, a retirement savings plan, paid holidays and paid time off. Compensation ranges may differ based on contract value but will be commensurate with job duties and relevant work experience. An applicant's salary history will not be used in determining compensation. Maximus will comply with regulatory minimum wage rates and exempt salary thresholds in all instances.
Accommodations
Maximus provides reasonable accommodations to individuals requiring assistance during any phase of the employment process due to a disability, medical condition, or physical or mental impairment. If you require assistance at any stage of the employment process\-including accessing job postings, completing assessments, or participating in interviews,\-please contact People Operations at [email protected] .
Minimum Salary
$90,400\.00
Maximum Salary
$129,400\.00
Salary Context
This $90K-$129K range is in the lower quartile 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 Maximus, 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 ($109K) sits 43% below the category median. Disclosed range: $90K to $129K.
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.
Maximus AI Hiring
Maximus has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Tysons, VA, US. Compensation range: $85K - $159K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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