Interested in this AI/ML Engineer role at SCAN Health Plan?
Apply Now →Skills & Technologies
About This Role
Founded in 1977 as the Senior Care Action Network, SCAN began with a simple but radical idea: that older adults deserve to stay healthy and independent. That belief was championed by a group of community activists we still honor today as the “12 Angry Seniors.” Their mission continues to guide everything we do.
Today, SCAN is a nonprofit health organization serving more than 500,000 people across Arizona, California, Nevada, New Mexico, Texas, and Washington, with over $8 billion in annual revenue. With nearly five decades of experience, we have built a distinctive, values\-driven platform dedicated to improving care for older adults.
Our work spans Medicare Advantage, fully integrated care models, primary care, care for the most medically and socially complex populations, and next\-generation care delivery models. Across all of this, we are united by a shared commitment: combining compassion with discipline, innovation with stewardship, and growth with integrity.
At SCAN, we believe scale should strengthen—not dilute—our mission. We are building the future of care for older adults, grounded in purpose, accountability, and respect for the people and communities we serve.
The Job
In this role, you will help advance SCAN’s AI enablement and enterprise data governance practices. This role is responsible for improving the trust, usability, and business context richness of data that powers AI, analytics, reporting, and operational decision\-making.
This role helps build the foundation for enterprise AI by strengthening core knowledge management capabilities via the development, governance, and adoption of enterprise data assets and knowledge structures, including business glossaries, governed metrics, semantic layers, ontologies, knowledge graphs, metadata, lineage, and stewardship practices. You will work closely with data stewards, subject matter experts, platform teams, and executive business leaders to build context, standardize business terminology, improve data quality, and make enterprise data easier for people and AI\-enabled tools to understand and use.
This is an essential role as SCAN modernizes its data ecosystem through a modern platform, scalable self\-service analytics, and AI\-assisted ways of working.
You Will
- Enhance SCAN’s semantic foundation by developing dimensional models, hierarchies, domain relationships, and governed metrics that enable consistent AI use cases.
- Support ontology and knowledge graph initiatives by identifying key business entities, relationships, synonyms, and domain concepts that improve data connectivity, searchability, and AI comprehension.
- Govern and document data assets across Snowflake, Databricks, and Iceberg environments to ensure structured and semi\-structured data is well\-described, discoverable, and trusted for analytics and AI workflows.
- Maintain and enrich the enterprise data catalog, documenting metadata, lineage, ownership, usage context, and business meaning for high\-value data assets.
- Improve data discoverability and usability through tagging, glossary management, workflow optimization, and metadata quality controls.
- Perform data and metric validation to assess accuracy, completeness, aggregation logic, consistency, and timeliness across data products, including via the use of AI\-assisted tools.
- Partner with business stakeholders, data stewards, subject matter experts, analytics teams, and technical teams to execute enterprise data governance and AI\-readiness priorities across key domains.
- Investigate data quality issues, conduct root cause analysis, document business impact, and partner with data engineering, analytics, reporting, and business teams to support resolution.
- Participate in Agile ceremonies to ensure governance and AI enablement work is visible, prioritized, and delivered effectively.
- Drive continuous improvement in governance automation, stewardship workflows, reporting certification, and AI\-assisted governance capabilities.
- We seek Rebels who are curious about AI and its power to transform how we operate, improve decision\-making, and serve our members.
- Actively support the achievement of SCAN’s Vision and Goals.
- Other duties as assigned.
Your qualifications
- Bachelor's degree in information systems, Data Analytics, Computer Science, Business Administration, Healthcare Administration, Statistics, or a related field; equivalent experience may be considered.
- 3\+ years of experience in data engineering, data governance, data management, data quality, analytics, business intelligence, metadata management, knowledge management, or related data\-focused role.
- Experience in the health care industry, especially in areas such as Medicare Advantage, clinical analytics, medical economics, quality, pharmacy, claims, provider, member, or operational data environments, is strongly preferred.
- Experience with modern knowledge management techniques such as semantic layers, ontologies, knowledge graphs, retrieval\-augmented generation, AI\-assisted documentation, or metadata\-driven discovery preferred.
- Familiarity with modern warehouse and open Lakehouse concepts, including cloud object storage, open table formats such as Apache Iceberg or Delta Lake, metadata catalogs, data sharing, lineage, and governance of structured and semi\-structured data.
- Familiarity with using AI\-assisted tools to improve documentation, analysis, data discovery, metadata quality, and governance workflows.
- Experience with enterprise data catalog, metadata management, data lineage, business glossary, stewardship, and/or or report certification practices.
- Experience with BI and reporting platforms such as Power BI, Tableau, SSRS, or similar tools.
- Experience with CLI tools such as Cortex Code, Claude Code, or similar.
- Experience working in SAFe Agile or another Agile delivery framework.
- Preferred certifications include CDMP, DGSP, DCAM, DAMA\-related training, Snowflake certification, Microsoft Power BI certification, or relevant Agile/SAFe certifications.
Competencies
- Knowledge Management Mindset: Helps organize business meaning, definitions, relationships, ownership, and metadata so that enterprise data is easier to find, understand, govern, and reuse.
- Business Partnership: Builds trusted relationships with business and technical stakeholders and translates governance, data quality, and AI\-readiness practices into business value.
- Analytical Thinking: Uses structured analysis to evaluate data issues, identify root causes, assess business impact, and improve trust in data used for analytics and AI\-enabled capabilities.
- Governance Discipline: Applies consistent standards for definitions, metadata, quality rules, stewardship, lineage, and documentation.
- Communication: Explains complex data concepts clearly to both technical and non\-technical audiences.
- Continuous Improvement: Identifies opportunities to improve governance workflows, data usability, automation, AI\-assisted documentation and scalable self\-service analytics.
Skills \& Abilities
- Ability to support semantic layer and metrics governance, including standardized definitions, dimensional concepts, hierarchies, measure consistency, and policy adherence.
- Familiarity with knowledge management concepts such as ontologies, knowledge graphs, taxonomies, and domain models.
- Ability to translate technical data structures into clear, business\-aligned definitions and documentation, especially via data dictionaries, catalog entries, report inventories, data quality findings, and usage context.
- Strong SQL skills for data profiling, validation, reconciliation, and issue investigation.
- Familiarity with Snowflake, SQL Server, Power BI, Tableau, SSRS, data catalogs, modern analytics platforms, and open lakehouse concepts such as cloud object storage, open table formats, metadata catalogs, and governed data access.
- Ability to work effectively across business, analytics, data engineering, platform, stewardship, and leadership teams.
- Practical understanding of healthcare data domains such as claims, membership, providers, clinical operations, pharmacy, quality, utilization management, or finance preferred.
- Ability to manage multiple priorities in an Agile environment while maintaining attention to detail.
- Awareness of emerging automation, AI\-assisted documentation, data quality monitoring, semantic search, and governed by self\-service analytics capabilities.
What's in it for you?
- Base Salary Range: $80,300 to $138,330 annually
- An annual employee bonus program
- Robust Wellness Program
- Generous paid\-time\-off (PTO)
- 11 paid holidays per year, 1 floating holiday, birthday off, and 2 volunteer days
- Excellent 401(k) Retirement Saving Plan with employer match
- Robust employee recognition program
- Tuition reimbursement
- An opportunity to become part of a team that makes a difference to our members and our community every day!
We're always looking for talented people to join our team! Qualified applicants are encouraged to apply now!
At SCAN we believe that it is our business to improve the state of our world. Each of us has a responsibility to drive Equality in our communities and workplaces. We are committed to creating a workforce that reflects our community through inclusive programs and initiatives such as equal pay, employee resource groups, inclusive benefits, and more.
SCAN is proud to be an Equal Employment Opportunity and Affirmative Action workplace. Individuals seeking employment will receive consideration for employment without regard to race, color, national origin, religion, age, sex (including pregnancy, childbirth or related medical conditions), sexual orientation, gender perception or identity, age, marital status, disability, protected veteran status or any other status protected by law. A background check is required.
\#LI\-JB1 \#LI\-Hybrid
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
*The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60\-1\.35(c)*
Salary Context
This $80K-$138K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At SCAN Health Plan, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($109K) sits 40% below the category median. Disclosed range: $80K to $138K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
SCAN Health Plan AI Hiring
SCAN Health Plan has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Long Beach, CA, US. Compensation range: $138K - $138K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.