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About This Role
Your Role
The AI \& Machine Learning team works in partnership across the enterprise to accelerate business outcomes by applying AI, machine learning, and generative AI to build intelligent products that create “intelligence at scale.” Reporting to the Director, AI \& Machine Learning, the Data Scientist, Principal will lead the development and deployment of novel applications that leverage generative AI models. This role focuses on rapidly developing new features and working across partner teams to deliver solutions and maximize impact, translating cutting\-edge AI research into real\-world products and taking features from 0 to 1\. You will design, build, and ship production\-grade AI products including LLM\-powered applications, AI agents and copilots, retrieval\-augmented generation (RAG) and search, and AI\-enabled automation embedded directly into customer\-facing applications and enterprise workflows such as claims, payment integrity, clinical insights, and member experience. You will set the technical direction for how AI is applied across the organization.
Our leadership model is about developing great leaders at all levels and creating opportunities for our people to grow – personally, professionally, and financially. We are looking for leaders that are energized by creative and critical thinking, building and sustaining high\-performing teams, getting results the right way, and fostering continuous learning.
Your Work
In this role, you will:
- Lead the development and deployment of novel applications that leverage generative AI models, setting the technical direction for AI and machine learning across the organization
- Design and develop scalable applications leveraging generative AI models including LLM applications, copilots, agents, and RAG and search, embedded in customer\-facing products and enterprise workflows
- Rapidly prototype new features and iterate based on evaluation results
- Lead the architecture and development of new products and features from 0 to 1
- Collaborate with researchers and product managers to translate cutting\-edge AI research into tangible product features
- Build the APIs, services, and data and retrieval pipelines that expose AI capabilities to applications
- Optimize software performance and ensure the reliability of deployed applications
- Champion best practices for building and deploying generative AI applications
- Evaluate model performance, analyze results, and implement improvements, ensuring responsible and compliant AI
- Mentor and develop team members, fostering a collaborative and high\-performing environment
Your Knowledge and Experience
- Bachelor’s degree in computer science, a quantitative discipline, or equivalent practical experience
- 8 years of experience in software development and applied AI/ML with a Bachelor’s degree; or 5 years with a Master’s; or a PhD with relevant experience
- Proven track record of building and shipping software products rapidly—not just developing models or analyses
- Strong software engineering skills and proficiency in Python, including building APIs and backend services
- Experience leading ML design and optimizing ML infrastructure—model deployment, evaluation, and data processing—and working with machine learning frameworks and libraries
- Hands\-on experience with deep learning and LLM application frameworks, including PyTorch, TensorFlow, LangChain, and LangGraph
- Hands\-on experience building applications that leverage generative AI models, including prompt engineering and retrieval\-augmented generation (RAG)
- Experience with generative AI research or applications preferred
- Experience designing agent\-based systems and orchestration frameworks preferred
- Experience with cloud computing platforms and infrastructure (e.g., Azure, Google Cloud, or AWS), and scalable data processing with SQL or Spark preferred
- Solid MLOps and LLMOps practices, including CI/CD, monitoring, and model lifecycle management preferred
- Experience rapidly developing and shipping software in a fast\-paced, customer\-facing environment, adapting to changing priorities preferred
Understanding of responsible AI and governance for regulated or healthcare environments preferred
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Hybrid
This role requires employees to be in\-office based on our hybrid workplace model, balancing purposeful in\-person collaboration with flexibility. For most teams, this means coming into the office two days each week.
Employees living more than 50 miles from an office location will work with their manager to determine in\-office time based on business need.
\#LI\-CM1
**ABOUT THE TEAM
About Stellarus and the Ascendiun Family of Companies**
Stellarus, launched in January 2025, is designed to scale innovative healthcare solutions that support customers in creating a health care experience deserving of their family, friends, and neighbors.
Stellarus is part of a family of organizations that is overseen by a nonprofit corporate entity named Ascendiun. The Ascendiun Family of Companies also includes Blue Shield of California and its subsidiary, Blue Shield of California Promise Health Plan and Altais, a clinical services company.
Stellarus’ vision is to empower its customers to create a healthcare experience that is worthy of their family, friends, and neighbors. Stellarus’ objective is to offer innovative, modern, scalable solutions that challenge the health care status quo. This very closely aligns with Blue Shield of California’s vision by using innovation to improve quality, affordability, and experience for members.
To achieve our mission, we foster an environment where all employees can thrive and contribute fully to address the needs of the various communities we serve. We are committed to creating and maintaining a supportive workplace that upholds our values and advances our goals.
Our Values:
At Stellarus, our core values of agility, trust, drive, courage and service shape our approach to developing innovative product offerings.
Our Workplace Model:
We believe in fostering a workplace environment that balances purposeful in\-person collaboration with flexibility \- providing clear expectations while respecting the diverse needs of our workforce. Our workplace model is designed around intentional in\-person interaction, collaboration, connection, creativity and flexibility:
- For most teams, this means coming into the office two days per week.
- Employees living more than 50 miles from an office location, out of state employees, and employees in certain member\-facing roles should work with their manager to determine in\-office time based on business need.
- For employees with medical conditions that may impact their ability to work in\-office, we are committed to engaging in an interactive process and providing reasonable accommodations to ensure their work environment is conducive to their success and well\-being.
The Company reserves the right to require more presence in the office based on business needs, and requirements are subject to change with periodic reviews.
Physical Requirements:
Office Environment \- roles involving part to full time schedule in Office Environment. Based in our physical offices and work from home office/deskwork \- Activity level: Sedentary, frequency most of work day.
Equal Employment Opportunity:
External hires must pass a background check/drug screen. Qualified applicants with arrest records and/or conviction records will be considered for employment in a manner consistent with Federal, State and local laws, including but not limited to the San Francisco Fair Chance Ordinance. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, national origin, sexual orientation, gender identity, protected veteran status or disability status and any other classification protected by Federal, State and local laws.
Salary Context
This $161K-$241K range is above the 75th percentile for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Blue Shield of California, 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 $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($201K) sits 5% above the category median. Disclosed range: $161K to $241K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Blue Shield of California AI Hiring
Blue Shield of California has 1 open AI role right now. They're hiring across Data Scientist. Based in Oakland, CA, US. Compensation range: $241K - $241K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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