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About This Role
JOB DESCRIPTION Job Summary
Provides senior level support for data science activities. Performs data and error analysis to improve models, and cleans and validates data for uniformity and accuracy. Executes data science and statistical analytical experiments methodically to solve various problems and positively impact various health care domains. Develops and deploys advanced machine learning models and artificial intelligence (AI) solutions that enhance Molina products and services. Leverages expertise in data science, machine learning, and AI technologies to derive insights from large datasets and create predictive models that drive business decisions. Pioneers generative AI health care solutions aimed at revolutionizing health care operations, and enhancing the member experience.
Essential Job Duties
- Extracts meaningful insights from complex datasets, identifies patterns, and interprets data to inform strategic decision-making.
- Designs, develops, and trains machine learning models using a variety of algorithms and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning.
- Develops and implements agentic workflows that utilize AI agents for autonomous task execution, enhancing operational efficiency and decision-making capabilities.
- Employs retrieval-augmented generation (RAG) patterns to improve the performance of language models - ensuring they can access and utilize external knowledge effectively to enhance their outputs.
- Fine-tunes pre-trained models for adaptation to specific tasks or datasets - ensuring optimal performance and relevance in various applications.
- Prepares data for analysis by performing data cleaning, handling missing values, and removing outliers to ensure high-quality inputs for modeling.
- Deploys AI models into production environments, monitors performance, and adjusts as necessary to maintain accuracy and effectiveness.
- Collaborates closely with cross-functional teams, including software engineers, product managers, and business analysts, to integrate AI solutions into existing systems and processes.
- Stays current with the latest advancements in AI and machine learning and apply insights to improve existing models and develop new methodologies.
- Creates comprehensive documentation of models, methodologies, and results; communicates findings clearly to non-technical stakeholders.
- Partners closely with business and other technology teams to build machine learning (ML) models to improve Star ratings, reduce care gaps and support other business objectives.
- Presents complex analytical information to all level of audiences in a clear and concise manner; collaborates with analytics team and assigns and manages delivery of analytical projects as appropriate.
- Identifies data solutions and technology enabled solution opportunities, and facilitates referrals to appropriate team members in building out payment integrity solutions.
- Leverages a broad range of tools and techniques to extract insights from current industry or sector trends.
- Provides training and support to new and existing data scientist team members.
Required Qualifications
- At least 6 years of experience as a data scientist, preferably in a health care environment, or equivalent combination of relevant education and experience.
- Bachelor's degree in IT, computer science, statistics, business, health care, engineering, etc.
- Knowledge of big data technologies (e.g., Hadoop, Spark).
- Strong programming skills in languages such as Python and R, and experience with machine learning frameworks like TensorFlow, Keras, or PyTorch.
- Excellent understanding of statistical methods and machine learning algorithms, including k-NN, Naive Bayes, SVM, and neural networks.
- Familiarity with designing and implementing agentic workflows that leverage AI agents for autonomous operations.
- Knowledge of retrieval-augmented generation (RAG) techniques and their application in enhancing AI model outputs.
- Proven experience in fine-tuning models for specific tasks, ensuring they meet the required performance metrics.
- Proficiency in data visualization tools (e.g., Tableau, Power BI) to present complex data insights effectively.
- Experience with SQL and NoSQL databases, data warehousing, and extract, transform, and load (ETL) processes.
- Strong analytical and problem-solving abilities, with a focus on developing innovative solutions to complex challenges.
- Critical-thinking and problem-solving skills.
- Ability to meet delivery deadlines.
- Strong time-management and organizational skills.
- Ability to collaborate cross-functionally in a highly matrixed organization.
- Effective verbal and written communication skills.
- Experience with Microsoft Project/Jira, Microsoft O365 Cloud (Exchange, Skype, SharePoint, OneDrive, OneNote), Microsoft Office/Office ProPlus (Excel, Word, PowerPoint, Outlook, Teams), Power BI, and applicable software programs proficiency.
Preferred Qualifications
- Experience with cloud platforms (e.g., Databricks, Snowflake, Azure AI Studio etc.) for working with AI workflows and deploying models.
- Familiarity with natural language processing (NLP) and computer vision techniques.
To all current Molina employees: If you are interested in applying for this position, please apply through the Internal Job Board.
Molina Healthcare offers a competitive benefits and compensation package. Molina Healthcare is an Equal Opportunity Employer (EOE) M/F/D/V
Salary Context
This $87K-$189K range is below the median for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Molina Healthcare, 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 $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($138K) sits 27% below the category median. Disclosed range: $87K to $189K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Molina Healthcare AI Hiring
Molina Healthcare has 1 open AI role right now. They're hiring across Data Scientist. Based in US. Compensation range: $189K - $189K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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