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About This Role
Director, Data Science and Informatics
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This position provides strategic and operational leadership for CareOregon’s Data Science, Data Engineering, and Business Intelligence functions within the Informatics and Evaluation (I\&E) department, reporting to the Vice President of I\&E within the Finance and Strategy division. The Director is accountable for setting vision, prioritization, and standards across technical teams, with a focus on developing people, maturing capabilities, and aligning work to organizational strategy, including the Healthcare Value workstream.
This role emphasizes leadership, cross\-functional partnership, and delivery of high\-impact analytical products rather than direct execution of analyses. This role oversees data platforms, data quality, and governance while driving the development of Data Science and AI capabilities. Partnering closely with business and technology stakeholders, the director ensures alignment on data definitions, enables trusted insights, and fosters a culture of innovation, efficiency, and data\-driven decision\-making across the organization.
This is a hybrid position with the expectation to be in office 2\-3x/per week. We are looking for candidates local to the Portland Metro area.Estimated Hiring Range:
$168,570\.00 \- $206,030\.00Bonus Target:
Bonus \- SIP Target, 10% Annual
Current CareOregon Employees: Please use the internal Workday site to submit an application for this job.
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Essential Responsibilities
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Technical/Operational Leadership
- Set technical vision, standards, and best practices across data science, data engineering, and business intelligence functions.
- Guide the development of advanced analytical capabilities, including predictive modeling, forecasting, and self\-service analytics.
- Oversee prioritization and delivery of analytics and data products, ensuring alignment with organizational priorities and business value.
- Oversee the development, maintenance, quality assurance, and documentation for the department’s data tables and data mart resources.
- Translate complex analytical concepts for leadership in the appropriate form to convey meaning, significance, and actionable information.
- Collaborate with internal and external customers to identify, refine, and deliver data products tailored to the customer needs and specifications.
- Oversee visual reporting (e.g., dashboards, scorecards, etc.) using data visualization tools such as Tableau when appropriate.
- Oversee maintenance of technical documentation such as data dictionaries, business glossaries, metric definitions, and other data resources.
- Prepare and/or deliver presentations to community groups, regulatory agencies, leadership, and other teams as needed.
Strategic/Operational Planning
- Develop strategies and team capabilities aligned with organizational priorities, including Healthcare Value initiatives.
- Provide input into strategic plans for the organization.
- Develop annual program goals that align with organizational strategic goals in collaboration with the Vice President of Informatics and Evaluation.
- Develop short\- and long\-term plans and policies; oversee the development and execution of standard operating procedures.
- Maintain a business unit view while establishing department priorities, being cognizant of broader business unit and organizational impacts.
- Own portfolio\-level prioritization across teams, ensuring resources are directed toward high impact initiatives.
- Create transparency into work content, team capacity, and tradeoffs to support department\-level prioritization.
Financial/Resource Management
- Recommend budgets in alignment with short\- and long\-term plans.
- Manage resources to ensure priorities are accomplished.
- Approve resource allocations within budget, including people, finances, and timelines; make decisions on exceptions.
Relationship Management
- Lead effective communication system for work group(s), ensuring a collaborative culture.
- Build and ensure effective relationships across internal teams and external organizations for current or future integration.
- Partner with internal leaders and managers in identifying improvement plans and processes.
- Provide department wide support and oversight in organizational alignment work with lines of business and regional teams; act as key liaison with other teams.
- Provide strategic partnership and support to lines of business to implement operational and clinical initiatives that assure that CareOregon meets all health outcomes quality improvement targets, performance improvement programs, and regulatory deliverables.
- Represent CareOregon in external meetings and functions, providing productive leadership presence and effectiveness.
- Act as CareOregon delegate in relevant state and national committees and ensure our programs and policies consider the evolving landscape.
- Foster a high\-performance culture with a focus on innovation, quality, and continuous improvement.
Employee Supervision
- Direct teams and establish team direction and goals in alignment with the organizational mission, vision, and values.
- Identify work and staffing models; recruit, hire, and oversee a team to meet work needs, using an equity, diversity, and inclusion lens.
- Identify department priorities; ensure employees have information and resources to meet job expectations.
- Lead the development, communication, and oversight of team and individual goals; ensure goals, expectations, and standards are clearly understood by staff.
- Manage, coach, motivate, and guide employees; promote employee development.
- Incorporate guidance from CareOregon equity tools into people leadership, planning, operations, evaluation, budgeting, resource allocation, and decision making.
- Ensure team adheres to department and organizational standards, policies, and procedures.
- Evaluate employee performance and provide regular feedback to support success; recognize strong performance and address performance gaps and accountability (corrective action).
- Perform supervisory tasks in collaboration with Human Resources as needed.
Experience and/or Education
Required
- Minimum 10 years’ experience in the programming and analysis of health insurance, medical claims and financial or health\-related related data
- Experience with health information, terminology and clinic outcomes or classification coding tools, CPT codes, ICD\-9/10
- Experience in leadership positions that include driving strategy, delivering impactful data engineering and data science initiatives for cross\-functional teams, and scaling data platforms
- Experience designing strategy in modern data architectures (data lakes, warehouses, ETL/ELT pipelines)
- Strong background in overseeing data governance, data quality, and enterprise data management
- Experience planning and leading complex analysis and reporting projects or any work experience and/or training that would likely provide the ability to perform the essential functions of the position
Preferred
- Minimum 4 years’ experience in a supervisory position
- Master’s degree in analytics, public health, or related field
- Experience working with the Oregon Health Plan (OHP) benefit, the Oregon Health Authority (OHA) and/or the Centers for Medicare and Medicaid Services (CMS) rules and regulations
- Experience with artificial intelligence, machine learning, and/or geographic information systems preferred
### Knowledge, Skills and Abilities Required
Knowledge
- Advanced knowledge of statistical software packages, such as Python, R, and SAS
- Knowledge of the basic concepts of Managed Care, Medicaid, and/or Medicare
- Knowledge of modern data platforms like Snowflake and Databricks
- Knowledge of AI and Data Science, with the ability to guide development of advanced analytics solutions and translate business needs into data\-driven insights
- Knowledge of Tableau Desktop/Creator or other Business Intelligence applications preferred Knowledge of healthcare claims
- Knowledgeable of current trends in clinical information systems and the associated technologies
- Knowledge of Agile development processes preferred
Skills and Abilities
- Ability to independently gather, compile and analyze data using statistical methods
- Ability to provide strong leadership, project management and oversight of analytical projects
- Ability to partner with business stakeholders to oversee data\-driven solutions and insights
- Ability to manipulate and analyze statistical data
- Ability to manage the workloads of others and interact effectively and motivate staff to perform at their highest level
- Ability to manage multiple tasks
- Ability to effectively synthesize and present actionable interpretations of complex information in both written and verbal form to internal and external stakeholders
- Excellent critical thinking and problem\-solving skills
- Ability and willingness to continually learn new skills
- Ability to learn, focus, understand, and evaluate information and determine appropriate actions
- Advanced skills with Microsoft Office applications including Access, Excel, and Word
- Ability to work effectively with diverse individuals and groups
- Ability to accept direction and feedback, as well as tolerate and manage stress
- Ability to see, read, and perform repetitive finger and wrist movement for at least 6 hours/day
- Ability to hear and speak clearly for at least 3\-6 hours/day
Working Conditions
Work Environment(s): Indoor/Office Community Facilities/Security Outdoor Exposure
Member/Patient Facing: No Telephonic In Person
Hazards: May include, but not limited to, physical and ergonomic hazards.
Equipment: General office equipment
Travel: May include occasional required or optional travel outside of the workplace; the employee’s personal vehicle, local transit or other means of transportation may be used.
Work Location: Office \- 3 days/week
We offer a strong Total Rewards Program. This includes competitive pay, bonus opportunity, and a comprehensive benefits package. Eligibility for bonuses and benefits is dependent on factors such as the position type and the number of scheduled weekly hours. Benefits\-eligible employees qualify for benefits beginning on the first of the month on or after their start date. CareOregon offers medical, dental, vision, life, AD\&D, and disability insurance, as well as health savings account, flexible spending account(s), lifestyle spending account, employee assistance program, wellness program, discounts, and multiple supplemental benefits (e.g., voluntary life, critical illness, accident, hospital indemnity, identity theft protection, pre\-tax parking, pet insurance, 529 College Savings, etc.). We also offer a strong retirement plan with employer contributions. Benefits\-eligible employees accrue PTO and Paid State Sick Time based on hours worked/scheduled hours and the primary work state. Employees may also receive paid holidays, volunteer time, jury duty, bereavement leave, and more, depending on eligibility. Non\-benefits eligible employees can enjoy 401(k) contributions, Paid State Sick Time, wellness and employee assistance program benefits, and other perks. Please contact your recruiter for more information.
We are an equal opportunity employer
CareOregon is an equal opportunity employer. The organization selects the best individual for the job based upon job related qualifications, regardless of race, color, religion, sexual orientation, national origin, gender, gender identity, gender expression, genetic information, age, veteran status, ancestry, marital status or disability. The organization will make a reasonable accommodation to known physical or mental limitations of a qualified applicant or employee with a disability unless the accommodation will impose an undue hardship on the operation of our organization.
Salary Context
This $168K-$206K range is above the median 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 CareOregon, 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. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($187K) sits 13% below the category median. Disclosed range: $168K to $206K.
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.
CareOregon AI Hiring
CareOregon has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Portland, OR, US. Compensation range: $206K - $206K.
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 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.
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