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About This Role
Job Summary
InnoVet Health is seeking an Applied Health Data Scientist with advanced analytics experience to support national AI initiatives across federal healthcare, with a primary focus on the Department of Veterans Affairs. This is a full‑performance‑level role: candidates must arrive with the technical judgment, operational maturity, and self‑sufficiency to contribute immediately within federal environments.
You will design and evaluate machine learning and LLM‑based solutions, build reproducible pipelines, assess data quality in complex health datasets, and translate analytical findings into actionable insights that improve Veteran care and reduce provider burden. Work includes AI governance, model evaluation, explainability, and continuous monitoring, ensuring all solutions are safe, trustworthy, and aligned with federal priorities and standards.
This role offers remote flexibility, competitive benefits, and the opportunity to shape the future of responsible AI in federal healthcare.
Responsibilities
AI/ML Development \& Evaluation
- Design and implement end‑to‑end ML pipelines, including ingestion, preprocessing, feature engineering, model selection, training, evaluation, and deployment, with clear rationale for design choices and tradeoffs.
- Conduct structured evaluations of LLM‑based and agentic AI approaches, including safety, hallucination, robustness, and workflow fit for federal healthcare use cases.
- Apply statistical and machine learning methods in Python and SQL to analyze large healthcare datasets and support use case validation.
- Develop reproducible analyses using version control (git) and experiment‑tracking tools (primarily MLflow).
Data Quality, Readiness \& Health Data Expertise
- Audit and assess incoming health data (EHR, claims, operational datasets) for missingness, inconsistency, structural irregularities, and bias; document principled decisions about data handling.
- Collaborate with data engineering and source system owners to improve upstream data quality, metadata, and data readiness for AI/ML workloads.
- Work within secure government cloud environments (AWS GovCloud, Azure Government) and distributed compute platforms (Databricks, Spark).
AI Governance, Safety \& Compliance
- Contribute to federal AI governance activities, including model documentation, risk assessments, and participation in internal review or oversight processes.
- Design evaluation plans that support continuous monitoring, drift detection, and re‑validation of AI systems in production.
- Ensure all work aligns with principles of explainability, fairness, privacy, and emerging federal AI policy, standards, and responsible‑AI guidance.
Stakeholder Engagement \& Workflow Integration
- Work with VA stakeholders to gather and refine requirements for advanced analytics and AI initiatives.
- Translate analytical and modeling outputs into clear, accurate visualizations and narratives tailored to technical, clinical, and executive audiences.
- Ensure AI solutions integrate into existing clinical and operational workflows, minimizing burden and maximizing adoption.
- Manage multiple concurrent projects across VA and other federal health clients, balancing deep technical work with stakeholder engagement and deliverable timelines.
Deliverables \& Federal Contract Execution
- Prepare formal federal deliverables including technical memos, evaluation reports, model cards, data management plans, and reproducibility documentation.
- Develop clear, defensible analyses suitable for audit, external review, and transition into federal environments.
Qualifications
Required
- Master’s degree in Data Science, Statistics, Computer Science, or a related quantitative field.
- 5\+ years of hands‑on experience in applied data science or machine learning, with a demonstrated track record of delivering work in real‑world, production, or contract environments.
- Proficiency in Python (primary language for all data science work) and SQL fluency, including comfort with T‑SQL and Databricks environments; R is a valued secondary skill for candidates from research or biostatistics backgrounds.
- Experience analyzing large and complex datasets. Experience with healthcare data, especially VA healthcare data, is preferred but not required.
- Familiarity with distributed computing platforms (e.g., Databricks, Spark) and secure government cloud environments (AWS GovCloud, Azure Government), or equivalent experience with commercial cloud systems (e.g., AWS, Azure, GCP) preferred.
- Familiarity with established data science and ML lifecycle frameworks (e.g., CRISP‑DM OSEMN, TDSP) and the ability to structure work using industry‑standard processes, documentation practices, and governance checkpoints.
- Exposure to large language models (LLMs) and agentic AI approaches, with the ability to evaluate potential use cases and limitations.
- Ability to clearly interpret and present results to both technical and non‑technical audiences.
- Ability to obtain and maintain VA suitability and a federal PIV badge.
- U.S. Citizen or Green Card holder.
- No 1099, corp‑to‑corp, or international outsourcing.
Preferred
- Direct experience working within the Department of Veterans Affairs on data science or adjacent projects.
- Broader healthcare data science experience across EHR, claims, imaging, or clinical NLP.
- Federal consulting experience and familiarity with government deliverables and FedRAMP environments.
- ML engineering capabilities (model versioning, monitoring, CI/CD, containerization).
Job Type: Full\-time
Pay: From $140,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Paid time off
- Referral program
- Vision insurance
Application Question(s):
- This position works with government contracts. Under Order 11935, either U.S. Citizenship or valid permanent residency is required. Answer 2 if you are a US citizen, 1 if you have a permanent resident card.
- Please provide the link to your LinkedIn account.
- Please provide the link to your GitHub account.
- How many years of experience do you have working on federal contracts?
- How many years of experience do you have with building, evaluating, and deploying ML models in production?
- How many years of experience do you have in using and evaluating Gen AI and Agentic AI solutions?
Education:
- Bachelor's (Required)
Experience:
- Python: 5 years (Required)
- healthcare data : 3 years (Required)
- SQL: 3 years (Required)
Work Location: Remote
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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At innoVet Health, 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 $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
innoVet Health, LLC AI Hiring
innoVet Health, LLC has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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