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About This Role
Index Analytics, LLC, is a rapidly growing, Baltimore\-based small business providing health\-related consulting services to the federal government. At the center of our company culture is a commitment to instilling a dynamic and employee\-friendly place to work. We place a priority on promoting a supportive and collegial team environment and enhancing staff experience through career development and educational opportunities.
Index Analytics is seeking a Data Scientist to support Government clients in the Baltimore and Washington D.C. Metro Area. This resource will create value from structured and unstructured data by applying domain knowledge, statistical analysis, and advanced machine learning techniques to solve complex healthcare challenges.
This role emphasizes end\-to\-end development of machine learning and AI systems, including traditional ML, deep learning, NLP, and modern LLM\-based architectures such as Retrieval\-Augmented Generation (RAG) and agentic AI systems.
Responsibilities* Design, develop, and maintain machine learning and deep learning models, including both traditional (e.g., regression, tree\-based models) and neural network\-based approaches.
- Build and deploy end\-to\-end ML pipelines on AWS (e.g., SageMaker, S3, Glue) for scalable training, evaluation, and inference.
- Develop and implement advanced NLP solutions, including text classification, entity recognition, topic modeling, and semantic search using models such as BERT and transformer\-based architectures.
- Design, build, and productionize RAG (Retrieval\-Augmented Generation) systems, including document ingestion, embedding pipelines, vector search, and LLM orchestration.
- Develop LLM\-powered applications, including prompt engineering, evaluation frameworks, and optimization techniques for accuracy, consistency, and cost.
- Contribute to agentic AI system design, including multi\-step reasoning workflows, tool use, and orchestration of LLM\-driven agents for complex tasks.
- Implement predictive analytics and statistical modeling to uncover patterns, trends, and insights from healthcare data.
- Perform data mining and exploratory data analysis (EDA) using state\-of\-the\-art techniques across structured and unstructured datasets.
- Build data visualizations, dashboards, and analytical tools to communicate findings clearly to technical and non\-technical stakeholders.
- Evaluate model performance using appropriate metrics (e.g., accuracy, AUC, precision/recall) and present results in a clear, actionable manner.
- Collaborate in an Agile environment with cross\-functional teams including engineers, analysts, and stakeholders.
- Recommend data\-driven solutions and AI strategies aligned with CMS business needs and healthcare policy objectives.
- U.S. citizen or otherwise authorized to work in the United States and able to demonstrate physical residency in the U.S. for at least three (3\) of the past five (5\) years. Must be able to obtain a U.S. Federal government client badge and pass a Public Trust clearance.
- Master’s degree in Computer Science, Data Science, or a related field required; PhD preferred.
- Three (3\) or more years of experience as a Data Scientist or in a similar role.
- Strong experience in machine learning and statistical modeling, including supervised and unsupervised learning techniques, deep learning, and a solid foundation in probability, hypothesis testing, and regression.
- Proven expertise in NLP and text analytics, including transformer\-based architecture (e.g., BERT and related models), embeddings, vector databases, and semantic search systems.
- Hands\-on experience building LLM\-powered applications, including prompt engineering, RAG architecture, and ideally agentic workflows or LLM orchestration frameworks, preferably within AWS environments (e.g., Bedrock).
- Advanced programming skills in Python (preferred) and/or R, with practical experience using ML and data libraries such as pandas, NumPy, scikit\-learn, PyTorch, and TensorFlow.
- Strong experience with AWS cloud and MLOps tooling, including SageMaker, S3, Glue, Airflow, and data stores such as Redshift and DynamoDB, along with version control (GitHub) and CI/CD pipelines (e.g., Jenkins).
- Experience with backend systems and data integration, including data modeling and supporting APIs for web\-based and production applications.
- Strong written and verbal communication skills, with the ability to explain complex models and insights clearly.
Attention Candidates
We're dedicated to ensuring a safe and transparent recruitment process for all candidates and have implemented robust measures to protect your personal information. Please be aware that all employment\-related communications will originate from a secure portal (NAME@msg.paycomonline.com) or a corporate email address (NAME@index\-analytics.com). If you have any concerns, please don't hesitate to reach out to us at recruiting@index\-analytics.com.
*If you are selected for an interview, please be advised that Index Analytics LLC reserves the right to prohibit the use of artificial intelligence (AI) tools, including but not limited to AI\-generated responses, real\-time transcription, or automated assistance during the interview process. We value authentic interactions and the opportunity to engage directly with candidates. Any unauthorized use of AI may result in disqualification from consideration.*
The salary range provided represents the estimated compensation for new hires in this position, applicable across all locations. Actual offers may vary based on factors such as the candidate's skills, qualifications, experience, and market conditions. Index complements its base salary offering with a competitive package that includes health and retirement benefits, discretionary bonuses, and reimbursement for professional development opportunities.
Index Analytics provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.
Salary Context
This $102K-$144K range is in the lower quartile for Data Scientist roles in our dataset (median: $166K across 345 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 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Index Analytics LLC, 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 $204,700 based on 441 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($123K) sits 40% below the category median. Disclosed range: $102K to $144K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Index Analytics LLC AI Hiring
Index Analytics LLC has 1 open AI role right now. They're hiring across Data Scientist. Based in Windsor Mill, MD, US. Compensation range: $144K - $144K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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