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About This Role
Alignment Health is breaking the mold in conventional health care, committed to serving seniors and those who need it most: the chronically ill and frail. It takes an entire team of passionate and caring people, united in our mission to put the senior first. We have built a team of talented and experienced people who are passionate about transforming the lives of the seniors we serve. In this fast\-growing company, you will find ample room for growth and innovation alongside the Alignment Health community. Working at Alignment Health provides an opportunity to do work that really matters, not only changing lives but saving them. Together.
The AI Scientist will play a key role in designing, developing, and deploying advanced Artificial Intelligence and Machine Learning solutions to support Alignment Healthcare’s business and operational goals. This position reports to an AI Science leader within the AI organization and works closely with cross\-functional partners across engineering, product, and business teams.
We are seeking highly skilled AI scientists at the Principal level who are passionate about applying state\-of\-the\-art AI and machine learning techniques to complex, real\-world healthcare problems. The ideal candidate brings strong technical expertise, sound judgment, and a proven ability to translate ambiguous business challenges into scalable, production\-ready AI solutions. This role requires a results\-oriented mindset, intellectual curiosity, and a collaborative approach to innovation.
This is a remote position within the US.Job Duties/Responsibilities
- Define technical strategy and architect enterprise AI capabilities (35%): Partner with VP of Data Science \& AI to establish technical roadmap and vision for organizational AI/ML capabilities. Architect enterprise\-wide AI platforms, frameworks, and systems that enable scalable AI adoption. Drive strategic AI initiatives with enterprise\-level impact such as autonomous clinical workflows, predictive member intervention engines, and AI\-powered risk adjustment optimization.
- Lead innovation and advance the state of healthcare AI (25%): Research, prototype, and validate novel ML approaches that create competitive advantages. Drive proof\-of\-concept initiatives for cutting\-edge AI technologies including LLM agents, causal inference for intervention effectiveness, and vision models for autonomous chart review. Create intellectual property through novel algorithms, proprietary methodologies, or technical frameworks.
- Influence organizational direction and technology decisions (20%): Advise executive leadership on AI strategy, technical feasibility, and risk. Shape organizational capability by influencing build\-vs\-buy decisions, technology stack choices, and platform investments. Partner with Engineering, Product, Clinical, and Operations leaders to embed AI into core business processes and drive enterprise alignment on technical initiatives.
- Establish technical excellence standards across the organization (10%): Set standards for code quality, MLOps maturity, model governance, and ethical AI. Own the most complex technical challenges where solutions are undefined and require significant innovation. Create reusable platforms, libraries, and tools that accelerate team productivity and raise the technical bar.
- Build external recognition and strategic partnerships (10%): Represent Alignment's technical capabilities to external stakeholders including CMS, payers, technology partners, and academic institutions. Publish research, present at conferences, and build Alignment's reputation as a leader in healthcare AI. Develop partnerships with research labs, universities, or technology vendors to advance organizational capabilities.
Job Requirements
Experience
Required:
- 8\-12\+ years in data science, machine learning, or AI research with a track record of high\-impact contributions
- Demonstrated experience in architecting and delivering AI systems with enterprise\-wide or industry\-wide impact
- Proven ability to translate research into production systems that drive measurable business outcomes
- History of technical leadership: defining strategy, influencing architecture, shaping technical culture
- Track record of innovation through patents, publications, open\-source contributions, or novel ML applications
Preferred:
- Healthcare AI expertise with demonstrated impact on clinical, operational, or financial outcomes
- Medicare Advantage domain knowledge: risk adjustment, Stars, utilization management, care management
- Experience with regulatory environments: CMS, FDA, HIPAA, or AI governance
- Prior experience at leading AI\-native companies or research institutions
- Track record of thought leadership: conference presentations, peer\-reviewed publications, industry recognition
Education
Required:
PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, or related field
Training
Required:
Active engagement with ML research community and continuous learning in state\-of\-the\-art AI methods; deep knowledge of healthcare regulations, AI ethics, and responsible AI practices
Preferred:
Advanced training in specialized ML domains (e.g., causal inference, reinforcement learning, LLMs); executive communication and strategic leadership training
Specialized Skills
Required:
- Recognized expert\-level proficiency in Data Science with deep theoretical and applied knowledge across multiple domains
- Mastery of Machine Learning including supervised/unsupervised learning, deep learning, NLP, reinforcement learning, and causal inference
- Deep understanding of Generative AI, predictive modeling, and classical machine learning techniques such as boosting and random forests.
- Advanced Statistical Inference, experimental design, and ability to establish methodological standards
- Expert programming: Python, Java, SQL, or PySpark with proven ability to architect production\-grade, scalable ML systems.
- Advanced SQL and experience designing data architectures for complex healthcare applications
- Deep expertise with Databricks, MLOps platforms (MLflow, Unity Catalog), and cloud\-native AI systems
- Cutting\-edge AI capabilities: Large language models, multimodal AI, AI agents, retrieval\-augmented generation (RAG)
- Strategic understanding of healthcare business models, Medicare Advantage operations, and regulatory landscape (CMS, HIPAA)
- Exceptional communication and influence: ability to articulate AI roadmap and technical strategy to executive audiences and shape organizational direction
- Proven ability to translate technical capabilities into business value and assess technical risk, feasibility, and ROI
Preferred:
- Experience building ML platforms or infrastructure used across data science organizations
- Deep expertise in healthcare AI specialization: clinical NLP, medical imaging, risk prediction, or intervention optimization
- Multi\-agent AI systems and autonomous decision frameworks
- Contributions to major open\-source ML projects or industry\-recognized thought leadership
Licensure
Required: None
Preferred: Industry recognition through certifications, awards, or fellowships in AI/ML
Essential Physical Functions
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is regularly required to talk or hear. The employee regularly is required to stand, walk, sit, use hand to finger, handle or feel objects, tools, or controls; and reach with hands and arms. The employee frequently lifts and/or moves up to 10 pounds. Specific vision abilities required by this job include close vision and the ability to adjust focus.
Pay Range: $198,219\.00 \- $297,329\.00
Pay range may be based on a number of factors including market location, education, responsibilities, experience, etc.
Alignment Health is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, age, protected veteran status, gender identity, or sexual orientation.
- *DISCLAIMER: Please* *beware of recruitment phishing scams affecting Alignment Health and other employers where individuals receive fraudulent employment\-related offers in exchange for money or other sensitive personal* *information. Please* *be advised that Alignment Health and its subsidiaries will never ask you for a credit card, send you a check, or ask you for any type of payment as part of consideration for employment with our company. If you feel that you have been the victim of a scam such as this, please report the incident to the Federal Trade Commission at* *https://reportfraud.ftc.gov/\#/. If you would like to verify the legitimacy of an email sent by or on behalf of Alignment Health’s talent acquisition team, please email* *[email protected].*
Salary Context
This $198K-$297K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 245 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 4,133 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Alignment 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 $198,000 based on 868 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($247K) sits 25% above the category median. Disclosed range: $198K to $297K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Alignment Healthcare AI Hiring
Alignment Healthcare has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Orange, CA, US. Compensation range: $195K - $297K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% of all AI roles offer remote work.
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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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