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About This Role
Savas Software/Lifekind Health is seeking a technically strong, impact\-driven Data Scientist with experience building ML\-based predictive products and advanced analytics (including LLM based) in real\-world environments. In this role, you will work with diverse and complex healthcare datasets—EHR, scheduling, billing, claims, structured \& unstructured clinical data—to design, train, and deploy machine learning models that directly influence patient care, operational performance, and clinical efficiency.
This is a high\-ownership, hands\-on role where you’ll help shape our intelligent data platform, build production\-ready features, experiment with models, and collaborate with engineering teams to deploy AI products. If you enjoy solving messy, high\-impact healthcare problems using AI, this role is for you.
This is not a remote position. You must live in the Scottsdale, AZ area and work in our office3 days per week. Relocation assistance is not available. Visa sponsorship is not available.
Our mission is to bring care that’s whole, human, and healing. Blending medical, behavioral, and lifestyle support into a single plan because restoring life takes more than a prescription.
Savas Software is a pioneering healthcare technology company dedicated to transforming clinical operations through innovative, integrated software solutions. Our mission is to empower healthcare organizations with tools that streamline workflows, enhance patient care, and ensure operational continuity. Through a unified approach to development, support, architecture, and enablement, we help clinics focus on what matters most—patient outcomes.
Machine Learning \& Predictive Analytics:
- Develop and deploy AI/ML models that power key products such as:
- Procedure Appropriateness
- Patient no\-show prediction
- Appointment optimization
- Clinical risk stratification
- Patient adherence forecasting
- Providerutilizationand throughput prediction
- Perform feature engineering using clinical, operational, and financial data
- Experiment with algorithms (tree\-based models, GLMs, ensemble methods, NLP, deep learning whereappropriate)
- Evaluate models using rigorous statistical and ML performance metrics
- Collaborate with ML Engineering to productionize models on Azure
Technical Environment (Azure AI/ML \& Analytics):
You’ll work within a modern AI/ML and analytics stack, including:
- LLMs:Open AI, Anthropic Claude
- Core Languages:Python, SQL
- Libraries \& Frameworks:Scikit\-learn,XGBoost,LightGBM, Pandas, NumPy, NLP libraries
- Visualization:Power BI, Plotly, Matplotlib, Seaborn
Data Analysis \& Insights:
- Conduct exploratory data analysis (EDA) on EHR, scheduling, billing, and procedural data to uncover trends, biases, and quality issues
- Translate clinical guidelines and workflows into computable, data\-driven logic
- Generate actionable insights that drive clinical and operational decision\-making
Data \& Feature Pipelines:
- Transform raw healthcare data into modeling\-ready datasets (structured \+ unstructured)
- Implement data validation, quality checks, and scalable transformation logic
- Collaborate with Data Engineering to ensure high\-quality, well\-governed data pipelines
LLMs, NLP \& Unstructured Data (Nice\-to\-Have but Valuable):
- Work with LLMs (Open AI, Anthropic Claude) to research and conceptualize recommendations
- Apply basic NLP techniques to extractsignalfrom clinical notes and operational text
- Explore entity extraction, rule\-based labeling, embedding\-based features, etc.
Visualizations \& Storytelling:
- Create dashboards and data visualizations using Power BI or Python to communicate insights
- Present findings and recommendations to clinicians, operations leaders, and executives
What Success Looks Like:
- Production\-ready ML models that drive measurable improvements in clinical operations
- High\-quality datasets, features, and reproducible pipelinespoweringour AI platform
- Actionable insights that influence patient outcomes and reduce operational friction
- Ability to independently drive complex data projects end\-to\-end with minimal supervision
Our Ideal Candidate will have the following qualifications:* 2 or more years of experience in data science, machine learning, or applied analytics
- Strong Python \+ advanced SQL skills for data manipulation, modeling, and EDA
- Experience developing and evaluating ML models in real\-world environments
- Experience with healthcare datasets (EHR, claims, clinical notes, billing, scheduling) is a strong advantage
- Familiarity with HIPAA, PHI handling, and healthcare data governance
- Strong understanding of feature engineering, statistical methods, and model validation
- Ability to clearly communicate technical concepts to non\-technical stakeholders
- Exposure to Prompt Engineering and working with LLMs (Open AI, Anthropic Claude) preferred
- Experience with Azure Data Factory, Azure Functions, Azure Open AI preferred
- Master’s degree in Data Science, CS, Statistics, Biomedical Informatics, or related field preferred
Generous salary and benefits package includes:* Medical, dental, and vision coverage options for you and eligible dependents
- Free basic Life/AD\&D, Short\-Term, and Long\-Term Disability policies for those enrolled in medical, plusadditionalvoluntary coverage options
- 401(k) Retirement plan
- Medical and Dependent Care Flexible Spending Accounts
- Generous vacation, sick, and holiday benefits
Lifekind Health and Savas Software are an Equal Opportunity Employer. We value a diverse workforce and inclusive workplace. People of color, people with disabilities, and lesbian, gay, bisexual, and transgender people are encouraged to apply. We consider all applicants without regard to race, color, ancestry, religion, gender, gender identity, gender expression, national origin, age, disability, socio\-economic status, marital or veteran status, pregnancy status or sexual orientation.
Salary Context
This $80K-$120K 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 Lifekind 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 $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 ($100K) sits 51% below the category median. Disclosed range: $80K to $120K.
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.
Lifekind Health AI Hiring
Lifekind Health has 1 open AI role right now. They're hiring across Data Scientist. Based in Scottsdale, AZ, US. Compensation range: $120K - $120K.
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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