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About This Role
##### Overview
Location: Remote, U.S.\-based
Type: Full\-time technical role
##### An Industry Defining Company
Karoo is a venture\-backed startup, on a mission to transform cardiovascular care. Our platform is designed to empower large provider groups and health systems to succeed in value\-based care contracts with payers by improving patient outcomes and reducing cost of care through better coordination, analytics, and disease management.
This is a rare opportunity to join a mission\-driven founding team at a pivotal stage and help define the future of healthcare technology, with the power to improve the lives of patients with cardiovascular disease, the number one cause of death in America today.
##### The Role
Karoo Health is seeking a Data Scientist to bridge the gap between complex healthcare data and actionable clinical and business insights. In this role, you will lead the development of predictive models and advanced analytics that drive our value\-based care mission. You will be a key part of our multidisciplinary team, working closely with Data Engineering, Product, and Clinical teams to build the "brain" of the Karoo platform.
A successful Data Scientist must be a curious problem solver who thrives in the "build" phase of a startup. You are equally comfortable writing production\-grade code as you are explaining a complex model's output to a clinical or business stakeholder.
##### What You'll Do
- Predictive Modeling \& Risk Stratification: Develop and deploy machine learning models to identify high\-risk, high\-cost members before they require expensive acute services (e.g., ER visits or inpatient stays).
- Clinical Intelligence: Complete research on claims and EHR data to identify opportunities for new clinical interventions to reduce risk of adverse events.
- Value\-Based Care Analytics: Lead the analysis of total cost of care (TCoC), utilization, and patient attribution to measure performance against baseline contracts.
- Data Quality \& Validation: Implement rigorous validation techniques to ensure model accuracy, fairness, and consistency, particularly when dealing with "noisy" clinical and claims data.
- Cross Collaboration: Work with Engineering and Analytics team members to provide modeling insights to be surfaced in applications and BI reports.
- Safeguard Data: Ensure strict compliance with healthcare data regulations (e.g., HIPAA, HITRUST) and implement data security best practices.
##### What We're Looking For
- Education: Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or a related quantitative field.
- Experience: Minimum 5 years of professional experience in an advanced analysis or Data Science role.
- Healthcare Domain: Experience with payer claims files and other healthcare data formats (e.g., FHIR, HL7\).
- Technical Proficiency: Expert level Python/R and SQL skills.
- Modeling Expertise: Proven track record of building and deploying production\-grade ML models (e.g., regression, classification, clustering, neural networks, etc).
- Regulatory Knowledge: Familiarity with HIPAA and HITRUST compliance requirements for managing sensitive patient data.
- Communication: Exceptional ability to translate complex technical findings into clear narratives for executive and clinical audiences.
- Must be legally authorized to work in the United States and not require employer sponsorship now or in the future
##### Why Join Us
- Competitive salary and equity in an early\-stage startup.
- Opportunity to define the technical solution from the ground up
- Fully remote work environment with flexible hours
- A collaborative and transparent company culture that values creativity and ownership
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 Karoo Health, 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.
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.
Karoo Health AI Hiring
Karoo Health 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 $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.
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