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About This Role
Requisition Number: 105937
Principal Data Scientist
Focus
Clinical / HLS, Google Cloud Gen AI, Agents, and Applied ML
Location
Nashville, TN area preferred
Insight at a Glance
- 14,000\+ engaged teammates globally
- $8\.2 billion in revenue in 2025
- Certified as a Great Place to work in 9 Countries in 2025
- Fortune 500 Company (No. 447\) in 2025
- Received 25\+ industry and partner awards in the past year
- $1\.4M\+ total charitable contributions in 2024 by Insight globally
About the Role
Now is the time to bring your expertise to Insight. Healthcare and life sciences organizations are moving quickly to adopt generative AI, machine learning, and agentic systems, but many still face a critical challenge: converting complex clinical data and operational workflows into safe, measurable, production\-ready AI solutions.
We are seeking a Principal Data Scientist with deep experience in clinical or healthcare and life sciences environments, Google Cloud generative AI, agentic AI patterns, and applied machine learning. In this client\-facing consulting role, you will help healthcare organizations design, validate, and operationalize AI solutions that improve decision support, streamline workflows, and unlock value from structured and unstructured clinical data.
You will bridge the gap between clinical stakeholders, technical engineering teams, and executive leadership, ensuring that AI solutions are not only innovative, but also responsible, explainable, secure, and aligned to healthcare business outcomes.
What You'll Do
- Clinical AI Solution Design: Lead the design of AI and ML solutions for healthcare and life sciences use cases, including clinical decision support, workflow automation, operational intelligence, patient\-facing insights, and knowledge retrieval across complex healthcare data environments.
- Google Cloud Gen AI Architecture: Design and guide implementation of generative AI solutions using the Google Cloud AI ecosystem, including Vertex AI, Gemini, model evaluation workflows, retrieval\-augmented generation patterns, and enterprise\-grade deployment approaches.
- Agentic Systems for Healthcare Workflows: Architect and prototype agentic AI solutions that can reason across clinical, operational, and knowledge\-based workflows while maintaining appropriate controls, traceability, and human\-in\-the\-loop oversight.
- Applied Machine Learning: Develop and guide machine learning approaches for classification, prediction, summarization, entity extraction, document intelligence, and other healthcare\-relevant use cases using structured, semi\-structured, and unstructured data.
- Data Readiness and Clinical Context: Partner with client stakeholders to evaluate data quality, lineage, terminology, interoperability considerations, and clinical workflow fit before advancing AI use cases into production.
- Model Evaluation and Responsible AI: Define evaluation strategies for accuracy, relevance, bias, safety, drift, explainability, and clinical appropriateness, ensuring AI outputs can be trusted by healthcare stakeholders.
- Technical Advisory and Client Engagement: Serve as a senior technical advisor to client leaders, translating complex data science and Gen AI concepts into practical roadmaps, business value narratives, and implementation plans.
- Thought Leadership and Delivery Enablement: Mentor data scientists, engineers, and consultants while contributing reusable healthcare AI patterns, accelerators, evaluation frameworks, and delivery playbooks for Insight.
What We’re Looking For
- Experience: 10\+ years of experience in data science, machine learning, healthcare analytics, clinical AI, or applied AI solution delivery, ideally within consulting or enterprise client environments.
- Healthcare / HLS Domain Expertise: Strong understanding of clinical workflows, healthcare operations, clinical documentation, patient data, provider environments, payer/provider dynamics, or life sciences data use cases.
- Google Cloud AI Expertise:Hands\-on experience with Google Cloud AI and data services, especially Vertex AI, Gemini, BigQuery, document AI, model deployment, and enterprise ML workflows.
- Generative AI and Agentic AI:Practical experience designing Gen AI and agentic solutions, including prompt engineering, tool use, orchestration patterns, RAG architectures, guardrails, and human review workflows.
- Machine Learning Depth: Strong foundation in supervised and unsupervised learning, NLP, model evaluation, feature engineering, experimentation, and production ML lifecycle practices.
- Responsible AI Mindset: Understanding of healthcare data sensitivity, PHI protection, explainability, model risk, clinical validation, and governance expectations for AI\-enabled healthcare solutions.
- Consulting Mindset: Exceptional communication skills with the ability to translate clinical and technical complexity into business\-aligned recommendations for executives, clinical leaders, and technology teams.
Preferred Certifications
- Google Cloud / AI: Google Cloud Professional Machine Learning Engineer, Professional Data Engineer, or relevant Google Cloud AI certifications.
- Data Science / ML: Databricks Machine Learning, TensorFlow, or other relevant ML and analytics certifications.
- Healthcare / Governance: Certifications or training related to healthcare data, HIPAA, clinical analytics, Responsible AI, or AI governance are a plus.
What You Can Expect
We’re legendary for taking care of you, your family and to help you engage with your local community.
But what really sets us apart are our core values of Hunger, Heart, and Harmony, which guide everything we do, from building relationships with teammates, partners, and clients to making a positive impact in our communities.
Join us today, your ambITious journey starts here.
*Insight is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, sexual orientation or any other characteristic protected by law.*
*When you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process.*
*At Insight, we celebrate diversity of skills and experience so even if you don’t feel like your skills are a perfect match \- we still want to hear from you!*
*Insight does not accept unsolicited resumes from recruiters or employment agencies. Unsolicited resumes will be treated as direct applications from the candidate, and recruiters or agencies who submit candidates for this position without a prior, written vendor agreement will not be eligible for any form of compensation, even if the candidate is hired.*
The position described above provides a summary of some the job duties required and what it would be like to work at Insight. For a comprehensive list of physical demands and work environment for this position, click here.
Insight is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, sexual orientation or any other characteristic protected by law.
Posting Notes: TN \|\| Tennessee (US\-TN) \|\| United States (US) \|\| Data \& AI \|\| None \|\| US \- Nashville, TN; 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Insight, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Insight AI Hiring
Insight has 4 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Phoenix, AZ, US, Nashville, TN, US, TN, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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