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About This Role
Job ID REQ20177952 Category Information Technology
Data Scientist
USC Institute of Urology delivers comprehensive, multidisciplinary urologic care and advanced surgical services across a broad range of subspecialties. The Institute advances innovation by integrating clinical expertise with research, education, and new technologies to improve patient outcomes and care delivery. Through collaboration across USC and USC Health, the Institute supports translational work that brings novel approaches into practice. The USC Urology AI Center builds and deploys practical, reliable AI systems to support clinical operations and research within the Department of Urology. The Center focuses on applied AI engineering, including agentic, tool\-using LLM workflows built on established technologies and integrated into real\-world departmental processes.
USC Institute of Urology is seeking a Data Scientist.
In this role, the Data Scientist will support the design, development, testing, and deployment of agentic AI workflows within a healthcare environment. Based in the AI Center at the USC Institute of Urology, they will collaborate with a cross\-disciplinary team of clinicians, researchers, and engineers to translate clinical and operational needs into practical, AI\-driven solutions. Responsibilities include contributing to multi\-step, API\-driven workflows, integrating multi\-modal data sources, and ensuring system reliability and traceability. The role focuses on building scalable, production\-ready systems that enhance clinical operations, support research initiatives, and ultimately improve patient outcomes.
The successful candidate will be motivated, pro\-active, resourceful, and detail\-oriented. The position requires excellent organizational and communication skills with the ability to be decisive and adaptable. Candidates should have experience with agentic artificial workflows as well as a strong understanding of governance and security protocols for deploying AI solutions.
Essential job duties include, but are not limited to:
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- Build AI workflows that execute multi\-step tasks through APIs and internal databases.
- Integrate and normalize multiple data modalities to support workflow execution.
- Implement RAG and grounding controls to support traceability and reduce unsupported outputs.
- Design, troubleshoot and refine Agentic AI workflows and AI Agents to augment current healthcare applications.
- Collaborate with clinicians, researchers, and operational stakeholders to translate needs into safe, testable, and measurable deliverables
Required Qualifications:
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- BS/MS in Computer Science, Engineering, or equivalent practical experience.
- Strong Python, SQL programming skills, GitHub, and experience with ML frameworks (PyTorch, TensorFlow, etc).
- Full stack software development, experience with JavaScript, Node.js, QT, or similar
- Demonstrated experience building and deploying LLM applications beyond prototypes, including tool/function calling, workflow orchestration, structured outputs, and validation.
- Experience implementing grounding/retrieval and reliability evaluation (test cases, regression checks, monitoring) and familiarity with security and privacy best practices for sensitive data.
Preferred Qualifications:
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- Experience integrating structured and unstructured data to support workflow automation and incorporating multimodal inputs where applicable.
- Multi\-step workflow design with escalation and human\-in\-the\-loop safeguards.
- Agentic AI workflow implementation and design of AI Agents (n8n, LangChain, LangGraph, AutoGen, CrewAI, or similar).
- Cloud deployment, secure operations (AWS/Azure/GCP), APIs.
- Multi\-agent systems / planning / Reinforcement Learning fundamentals.
- Lightweight fine\-tuning or adaptation experience.
Follows established USC and department policies, procedures, objectives, performance improvement, attendance, safety, environmental, and infection control guidelines, including adherence to the workplace Code of Conduct and Compliance Plan.
This position is on\-site and employee must report to work at the USC Health Science Campus in Los Angeles, CA when scheduled. Work hours and on\-site days may be subject to change depending on business needs.
The annual budgeted range for this position is $120,213\.99 \- $155,525\.71\. 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.
Minimum Education: Bachelor's degree Additional Education Requirements Combined experience/education as substitute for minimum education Minimum Experience: 2 years Minimum Skills: Experience using statistical computer languages (e.g., R, Python, SQL) to manipulate data and draw insights from large data sets. Experience working with and creating data models and data architecture, and using data visualization tools (e.g., Tableau, ArcGIS, D3\.js). Knowledge of current data modeling tools and various machine\-learning techniques and algorithms (e.g., clustering, decision\-tree learning, artificial neural networks). Experience scripting and programming in several languages with common data science toolkits. Proficient use of query languages (e.g., SQL, MDX) and experience working with relational (e.g., MySQL, SQL Server, Oracle, Snowflake, Redshift) and non\-relational (e.g., Mongo, NoSQL) databases. Knowledge of applied, statistical concepts and techniques skills (e.g., distributions, statistical testing, regression). Excellent written and oral communication skills. Ability to provide both detailed information and summaries to management\-level individuals and groups. Experience developing customer relationships and delivering customer\-focused service. Proven problem\-solving and decision\-making skills, and the ability to uncover root cause and evaluate different solution options. Preferred Education: Bachelor's degree Preferred Experience: 4 years Preferred Skills: Bachelor's degree in applied math, data science, computer science, statistics, or related field. Experience in data science, analytics, IT, cognitive engineering, or related fields. Demonstrated interest in data science and artificial intelligence, and experience in open domain (e.g. GitHub). Published writing on artificial intelligence and/or data science. Ability to write high\-quality Python code. Familiarity with unit testing, source control and code review.
Job ID REQ20177952 Posted Date 07/17/2026
Salary Context
This $120K-$155K range is below the median 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 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 $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($137K) sits 29% below the category median. Disclosed range: $120K to $155K.
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.
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: $155K - $155K.
Location Context
AI roles in Los Angeles pay a median of $215,000 across 397 tracked positions.
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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