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About This Role
Posting Date
08/14/2026
2000 16th Street, Denver, Colorado, 80202, United States of America
Denver, Colorado \| Full\-Time \| On\-Site/Hybrid
About the Team
The Commercial Integrated Care Analytics team supports DaVita Integrated Kidney Care (DaVita IKC). DaVita IKC is the renal population health management division of DaVita serving approximately 70,000 end stage renal disease (ESRD) and late stage chronic kidney disease (CKD) patients across the U.S. A key component of DaVita’s integrated care strategy is our healthcare analytics team, responsible for developing and communicating advanced data analytics to inform program performance and model of care design.
As a member of this team, you will be responsible for designing and executing rigorous statistical analyses and data science work that measures the causal impact of our care model on clinical and cost outcomes. This entails developing inferential frameworks, building predictive models, and communicating evidence\-based findings that drive care program decisions and value\-based contract performance.
This role will work with teammates across various teams, ranging from analyst to clinicians and operators to executive leaders. The environment is highly collaborative and team\-oriented, with a differential focus on both professional and personal growth.
Essential Duties and Responsibilities
The analyst must be able to work successfully with cross\-functional teams and have the maturity to interact directly with both peers and leaders across departments. The following duties and responsibilities generally reflect the expectations of this position, but are not intended to be all\-inclusive.
Statistical Inference \& Study Design
- Design and execute observational studies using quasi\-experimental methods (e.g., difference\-in\-differences, propensity score matching, regression) to evaluate the impact of care interventions on clinical and cost outcomes
- Build and interpret regression models (e.g., negative binomial, logistic, mixed\-effects) to estimate treatment effects and identify drivers of utilization and cost
- Apply Bayesian or frequentist inference frameworks to quantify uncertainty in program effectiveness estimates
- Conduct power analyses and sample size calculations to support pilot study design and program evaluation planning
- Develop analytic designs that translate operational research questions into well\-specified studies, including defining appropriate comparison groups and controlling for confounders
Data Science \& Model Measurement
- Build, validate, and monitor predictive models (e.g., hospital readmission risk, disease progression, cost forecasting) that inform care management priorities and resource allocation
- Develop and maintain model performance measurement frameworks, including calibration, discrimination, and fairness metrics, to ensure models remain accurate and actionable over time
- Conduct feature engineering using clinical, claims, and demographic data to improve model performance and interpretability
- Evaluate and compare modeling approaches (e.g., penalized regression, gradient boosting, survival analysis) with attention to explainability and clinical relevance
- Partner with operations and clinical teams to translate model outputs into practical decision support tools and intervention triggers
AI Integration \& Workflow Innovation
- Incorporate large language models and AI\-assisted tooling into analytic workflows to accelerate code development, literature review, documentation, and exploratory analysis
- Evaluate emerging AI tools and methods for applicability to healthcare analytics use cases, including automated feature selection, synthetic data generation, and natural language processing of clinical notes
- Develop and share best practices for responsible AI use within the analytics team, including prompt engineering, output validation, and appropriate use\-case scoping
Communication \& Stakeholder Engagement
- Produce clear, technically sound documentation of analytic methods, assumptions, and limitations
- Develop executive\-level communications that translate complex statistical findings into actionable insights for clinical, financial, and operational stakeholders
- Partner with stakeholders to define technical and business requirements for reporting and analytic requests
- Leverage reporting and supporting data to monitor operations performance and identify insights around clinical, demographic, and medical cost trends and their contribution to outcomes
Characteristics and Competencies
- Build relationships with both internal and external partners and clients to ensure the success of IKC programs
- Embrace working in a fast\-paced environment with comfort navigating ambiguity
- Focus on analytical process improvement and methodological rigor
- A self\-starter mentality and thoughtful execution abilities
- Strong written and verbal communication skills, with the ability to convey technical concepts to non\-technical audiences
- Excellent organizational and prioritization skills
- Intellectual curiosity with demonstrated interest and strengths in data analysis, statistical methods, and innovation
Qualifications
- Master’s degree preferred in biostatistics, epidemiology, economics, statistics, data science, or a quantitative field
- Bachelor’s degree required in a quantitative discipline (statistics, mathematics, economics, computer science, public health, or related field)
- 2\-4 years of applied experience with causal inference, program evaluation, or health services research methods is required
- 1\+ year of experience with SQL and querying relational databases containing clinical or claims data is required
- Proficiency in Python or R for statistical modeling, not limited to data manipulation, is required
- Working knowledge of regression modeling for count, binary, and time\-to\-event outcomes
- Familiarity with healthcare claims data structures (medical, pharmacy, eligibility) preferred
Preferred / Nice to Have
- Experience with version control (Git) and reproducible research practices
- Experience with Tableau or similar analytics visualization tools
- Demonstrated comfort using AI/LLM tools (e.g., ChatGPT, Claude, Copilot) to enhance analytic productivity
- Familiarity with machine learning model evaluation and monitoring (e.g., AUROC, Brier scores, calibration curves)
- Experience developing presentations in PowerPoint or similar tools for executive audiences
- Excel proficiency including pivot tables and data manipulation
What to Expect When You Join Our Village
- A “community first, company second” culture based on Core Values that really matter
- Clinical outcomes consistently ranked above the national average
- Award\-winning education and training across multiple career paths to help you reach your potential
- Performance\-based rewards based on stellar individual and team contributions
- A comprehensive benefits package designed to enhance your health, your financial well\-being, and your future
- Dedication, above all, to caring for patients suffering from kidney failure and other chronic disease across the nation
What We’ll Provide
More than just pay, our DaVita Rewards package connects teammates to what matters most. Teammates are eligible to begin receiving benefits on the first day of the month following or coinciding with one month of continuous employment. Below are some of our benefit offerings.
- Comprehensive benefits: Medical, dental, vision, 401(k) match, paid time off, PTO cash out
- Support for you and your family: Family resources, EAP counseling sessions, access Headspace®, backup child and elder care, maternity/paternity leave and more
- Professional development programs: DaVita offers a variety of programs to help strong performers grow within their career and also offers on\-demand virtual leadership and development courses through DaVita’s online training platform Star Learning
At DaVita, we strive to be a community first and a company second. We want all teammates to experience DaVita as "a place where I belong." Our goal is to embed belonging into everything we do in our Village, so that it becomes part of who we are. We are proud to be an equal opportunity workplace and comply with state and federal affirmative action requirements. Individuals are recruited, hired, assigned and promoted without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, protected veteran status, or any other protected characteristic.
This position will be open for a minimum of three days.
The Salary Range for the role is $62,353\.20 \- $95,000\.00/year.
For location\-specific minimum wage details, see the following link: DaVita.jobs/WageRates
Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies and experience. DaVita offers a competitive total rewards package, which includes a 401k match, healthcare coverage and a broad range of other benefits. Learn more at https://careers.davita.com/benefits
Colorado Residents: Please do not respond to any questions in this initial application that may seek age\-identifying information such as age, date of birth, or dates of school attendance or graduation. You may also redact this information from any materials you submit during the application process. You will not be penalized for redacting or removing this information.
Beware of Recruitment Fraud
DaVita will never ask for payment or personal financial information at any point in the hiring process, nor will we ever communicate with you using email addresses outside of the DaVita secure network. If you receive a request like this, it is not legitimate. Do not share your information.
Career Growth \- From clinical and operations to corporate jobs, our learning and development programs help guide your career journey.
Salary Context
This $62K-$95K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At DaVita, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($78K) sits 63% below the category median. Disclosed range: $62K to $95K.
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.
DaVita AI Hiring
DaVita has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $95K - $95K.
Location Context
AI roles in Denver pay a median of $199,950 across 66 tracked positions. That's 7% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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