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About This Role
Job ID REQ20176309 Category Research and Clinical Support
In order for your application to be considered, you must attach a cover letter and resume to your employment application.
The Leonard D. Schaeffer Center for Health Policy \& Economics is seeking a Research Data Scientist (HR title: Research Programmer II) to support cutting\-edge health policy and economics research through advanced analytics, data infrastructure development, and project leadership. This role is part of a state\-of\-the\-art data core and collaborates closely with principal investigators and a multidisciplinary research team. This is a full\-time, one\-year fixed term position (renewable) with a hybrid work arrangement.
The mission of the Schaeffer Center is to measurably improve value in health through evidence\-based policy solutions, research excellence, transformative education, and private\- and public\-sector engagement. This position offers the opportunity to contribute to work that has a meaningful impact on the transformation of health care and to apply advanced analytics to real\-world policy questions using large\-scale health care data.
Responsibilities:
- Lead and execute complex data analyses using large\-scale health care data (e.g., Medicare, Medicaid, commercial claims)
- Design and build data pipelines, research tools, and analytic infrastructure
- Ensure data quality and reproducibility through rigorous quality assurance processes
- Manage projects, including timelines, deliverables, and coordination with cross\-functional team members
- Supervise and mentor junior staff
- Translate complex data into actionable insights to support policy\-relevant research
Required Qualifications:
- MA/MS in statistics, data science, economics, applied mathematics, or related quantitative field (or equivalent combined education and work experience)
- 5\+ years of experience with statistical programming (e.g., SAS, R, Python)
- Experience with administrative health care claims data (Medicare, Medicaid, or commercial)
- Experience with SQL, data warehouses (e.g., Snowflake), and cloud platforms (e.g., AWS)
- Demonstrated experience leading analytic efforts and contributing to research projects
- Strong problem\-solving skills, with the ability to develop innovative solutions for complex data challenges
- Eligibility to work in the U.S. (no visa sponsorship available)
Preferred Qualifications:
- Experience in health economics, health policy, public health, or similar discipline
- 7\+ years of experience with statistical programming
- Experience with CMS secure environments (e.g., VRDC, IDR) and NIA LINKAGE.
- Experience leveraging AI\-assisted tools to enhance coding, data analysis, and workflow efficiency, including iterative or semi\-automated workflows
Minimum Education:
Master’s Degree
Compensation
The annual base salary range for this position is $106,012\.83 \- $125,000\. When extending an offer of employment, the University of Southern California considers factors such as (but not limited to) the scope and responsibilities of the position, the candidate’s work experience, education/training, key skills, internal peer equity, federal, state, and local laws, contractual stipulations, grant funding, as well as external market and organizational considerations.
Commensurate with experience and qualifications.
Position is 100% FTE
Performs other related duties as assigned or requested. The university reserves the right to add or change duties at any time.
Required Documents
Cover letter
Resume/CV
About the Schaeffer Center for Health Policy \& Economics
Since its establishment in 2009, the USC Schaeffer Center for Health Policy \& Economics has served as an intellectual hub for health policy and health economics research at USC. The Schaeffer Center’s mission is to measurably improve value in health through evidence\-based policy solutions, research excellence, and private and public sector engagement. The Center ranks 4th in the world in health economics, and is a trusted resource for Congress, federal agencies (including CMS, FDA, CBO, and NIH), and organizations such as the National Academies of Science, Engineering, and Medicine.
The Schaeffer Center is a collaboration between the USC Mann School of Pharmacy and Pharmaceutical Sciences and the USC Price School of Public Policy, and it is a flagship program of the Leonard D. Schaeffer Institute for Public Policy \& Government Service.
About the USC Schaeffer Institute
The USC Schaeffer Institute develops evidence\-based solutions to address the nation’s most pressing policy issues. It serves as a policy laboratory to develop and test ideas generated by the USC academic community, fosters civic engagement, and provides a forum to reach federal policymakers.
Established by an historic gift from Leonard D. Schaeffer in 2024, it houses three flagship programs: the USC Schaeffer Center for Health Policy \& Economics, the Leonard D. Schaeffer Fellows in Government Service, and the Center for Civic Society.
Schaeffer experts have testified before numerous Congressional committees and are regularly sought out by media outlets—including the New York Times, NPR, Stat, Wall Street Journal, and Washington Post—to provide insight on policy issues.
The Schaeffer Institute is home to more than 150 scholars and staff, including three Nobel Laureates and seven members of the National Academy of Sciences, Engineering, and Medicine. It has offices at USC in Los Angeles and at the USC Capital Campus in Washington, DC.
About the Sol Price School of Public Policy
Ranked among the foremost schools of public policy in the nation, the USC Sol Price School of Public Policy generates uncommon knowledge for the common good. The school is composed of overlapping disciplines that generate innovative approaches to critical issues ranging from health\-care policy to homelessness, and sustainability to congestion – to name a few. A wide\-ranging curriculum, including extensive experiential learning, prepares our graduates to navigate problems that demand multi\-layered solutions driven by critical, informed thinking.
The Price School, founded in 1929, is anchored by four departments: Public Policy and Management, Health Policy and Management, Wilbur H. Smith III Department of Real Estate Development, and Urban Planning and Spatial Analysis. The School’s rigorous academic programs provide students with the knowledge and distinctive opportunities to make meaningful contributions to their professions. Integrating classroom instruction with real\-world experience and led by some of the world’s most renowned faculty in their fields, our students establish a clear pathway to successful careers.
Our academic programs are augmented by numerous research centers, institutes and initiatives that provide additional research expertise and experiences, notable among them are: The Judith and John Bedrosian Center on Governance and the Public; The Center for Philanthropy and Public Policy; The USC Lusk Center for Real Estate; The METRANS Transportation Consortium; The Leonard D. Schaeffer Center for Health Policy \& Economics; and The Schwarzenegger Institute for State and Global Policy. Together, they account for over $100 million in externally funded research grants and contracts.
Together, these departments and research enterprises provide unmatched breadth and depth to tackle an enormous range of challenges facing our country and the world. Price graduates hold leadership positions across diverse sectors – public, private, and nonprofit – championing the advancement of the common good. They come from around the world and from a variety of cultures and socio\-economic backgrounds to create a rich intellectual environment that celebrates, supports and benefits from a variety of backgrounds and opinions.
About the Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences
USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences is located on the USC Health Sciences Campus, which includes the Keck Hospital of USC, the Norris Cancer Hospital, Doheny Eye Institute, and the flagship LAC\+USC Medical Center, along with directing five university\- and community\-based pharmacies, adding a sixth in South Los Angeles in 2023\. Ranked by US News and World Report as the \#1 private pharmacy school in the United States, and the \#2 overall school according to World Scholarship Forum, the USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences is recognized for its more than century\-long reputation for innovation and leadership in pharmaceutical education, practice, research, and service to the community and to the profession. The school uniquely spans the entire spectrum of pharmaceutical development and clinical care – from drug discovery to regulatory approaches that promote safety and innovation, from delivery of contemporary patient care services to evaluating the impact of care provision on patient outcomes and costs. The school’s dynamic faculty is involved in all levels of pharmacy education: from undergraduate majors to multiple MS and PhD graduate programs, from the Doctor of Pharmacy degree program with dual degree options to a wide variety of advanced clinical residency and fellowship training, and from laboratory to bedside learning experiences. The school has recently received a $50 million endowment from the Alfred E. Mann Foundation to name the school and to support student scholarships, faculty development and recruitment, and integrated biomedical innovation.
Minimum Education: Master's degree Addtional Education Requirements Combined experience/education as substitute for minimum education Minimum Experience: 3 years Minimum Skills: Relevant work experience to provide strong technical knowledge of programming and analysis as well as senior or lead experience. Demonstrated ability to stand in for researchers as circumstances require. Demonstrated creativity and innovation in solving conceptual programming problems. Preferred Experience: 5 years
Job ID REQ20176309 Posted Date 06/10/2026
Salary Context
This $106K-$125K range is in the lower quartile 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 University of Southern 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 $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($115K) sits 42% below the category median. Disclosed range: $106K to $125K.
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.
University of Southern California AI Hiring
University of Southern California has 1 open AI role right now. They're hiring across Data Scientist. Based in Los Angeles, CA, US. Compensation range: $125K - $125K.
Location Context
AI roles in Los Angeles pay a median of $191,580 across 1,792 tracked positions. That's 4% 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 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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