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About This Role
Posting Details
This job posting may be used to fill multiple vacancies.
Interviews are anticipated to be conducted within two weeks of closing date.
Our Vision
Every New Mexican has access to affordable health care coverage through a coordinated and seamless health care system.
Our Mission
We ensure that New Mexicans attain their highest level of health by providing whole\-person, cost\-effective, accessible, and high\-quality health care and safety\-net services.
Our Goals
Leverage purchasing power and partnerships to create innovative policies and models of comprehensive health care coverage that improve the health and well\-being of New Mexicans and the workforce.
Achieve health equity by addressing poverty, discrimination, and lack of resources, building a New Mexico where everyone thrives.
Implement innovative technology and data\-driven decision\-making to provide unparalleled, convenient access to services and information.
Build the best team in state government by supporting employees' continuous growth and wellness.
Why does the job exist?
The Manager, Data Science / Chief Data Scientist:
Leads the development and execution of data strategies that drive informed decision making and generate meaningful business insights;
Partners closely with Information Technologies (IT) and key business units to design innovative, data driven solutions aligned with the Health Care Authority's (HCA) strategic goals;
Serves as a trusted advisor for data driven insights, functioning as the head of analytics and solutions for the organization;
Works under the supervision of the HCA Chief Data and Analytics Officer to guide the strategic analytics function within the Office of Data and Analytics;
Ensures HCA remains at the forefront of advanced analytic solutions, supporting data driven decisions and program development;
Plays a key role in expanding HCA's capabilities in prescriptive and cognitive analytics;
Leads efforts to enhance analytic skills and data literacy across the organization;
Supports HCA's mission of implementing innovative technologies that provide convenient and unparalleled access to information and services.
How does it get done?
The Manager, Data Science / Chief Data Scientist is responsible for:
Serving as the department's lead expert and sharing best practices in statistics, data modeling, applied mathematics, quantitative research, and predictive modeling;
Developing relationships with cross departmental partners to build collaborative analytics efforts that support policy development, reduce inequality, and promote equity for New Mexicans;
Translating business needs into analytics and reporting requirements that support executive decisions and workflows;
Encouraging informed risk\-taking and acting as a catalyst for innovation by generating practical, sustainable, and creative solutions that maximize existing resources;
Performing large\-scale experimentation and building data driven models to address business questions;
Mining and analyzing large structured and unstructured datasets, identifying trends and patterns, defining and monitoring metrics, creating data narratives, and building tools that drive decisions;
Researching and implementing cutting\-edge machine learning, deep learning, and artificial intelligence techniques to increase efficiency in data analysis;
Providing technical support for internal studies, including outcome evaluations, process reviews, program implementation assessments, needs analyses, performance measurement, feasibility studies, and program monitoring;
Developing and maintaining technical knowledge in machine learning, generative AI, and related technologies while staying current with industry trends and best practices;
Ensuring effective design and high\-quality deliverables in the machine learning domain by designing and developing ML and deep learning systems, running tests, and implementing appropriate algorithms;
Applying statistical analysis and visualization techniques such as hierarchical clustering, tSNE, and PCA to support machine learning activities;
Developing and maintaining a training program in basic statistics and data analysis tools for new analysts and staff to strengthen the hub and spoke model and expand data driven decision\-making capacity;
Engaging in the development of data standards and data governance operations, including documenting business rules, metadata, procedures, and supporting quality improvement efforts across the organization.
Who are the customers?
The Manager, Data Science / Chief Data Scientist will:
Understand HCA's strategy and work to enable and develop innovative analytic solutions that advance HCA's mission and goals;
Engage with the CDAO, CIO, and the HCA Strategy Team, which includes the HCA Secretary, Deputy Secretaries, and Division Directors;
Collaborate with various program business leaders and division/spoke data teams to support strategic and analytic needs.
Ideal Candidate
The Manager, Data Science / Chief Data Scientist will:
Understand HCA's strategy and work to enable and develop innovative analytic solutions that advance HCA's mission and goals;
Engage with the CDAO, CIO, and the HCA Strategy Team, which includes the HCA Secretary, Deputy Secretaries, and Division Directors;
Collaborate with various program business leaders and division/spoke data teams to support strategic and analytic needs.
Minimum Qualification
Bachelor's degree in Business Administration, Science Technology, Engineering, and Mathematics (STEM), Computer Science, Data Science, Information Systems, Statistics, Physics, Economics, or related field and ten (10\) years of combined experience in cloud engineering, cloud migration, data engineering, data integration, streaming data development, data warehousing, data curation, data analysis, design, storage, data protection and security, or related field. Any combination of education from an accredited college or university in a related field and/or direct experience in this occupation totaling fourteen (14\) years may substitute for the required education and experience.
Employment Requirements
Must possess and maintain a valid Driver's License.
Must obtain a Defensive Driving Certificate.
Employment is subject to a pre\-employment criminal background investigation and is conditional pending results.
Supplemental Information
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Agency Contact Information: Theresa Romero, (505\) 394\-2977\. Email
For information on Statutory Requirements for this position, click the Classification Description link on the job advertisement.
Bargaining Unit Position
This position is not covered by a collective bargaining agreement.
Salary Context
This $106K-$170K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At State of New Mexico, 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 $198,000 based on 808 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $259,000. This role's midpoint ($138K) sits 30% below the category median. Disclosed range: $106K to $170K.
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.
State of New Mexico AI Hiring
State of New Mexico has 1 open AI role right now. They're hiring across Data Scientist. Based in Albuquerque, NM, US. Compensation range: $170K - $170K.
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 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 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).
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 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
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