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About This Role
Join the transformative team at City of Hope, where we're changing lives and making a real difference in the fight against cancer, diabetes, and other life\-threatening illnesses. City of Hope’s growing national system includes its Los Angeles campus, a network of clinical care locations across Southern California, a new cancer center in Orange County, California, and treatment facilities in Atlanta, Chicago and Phoenix. Our dedicated and compassionate employees are driven by a common mission: To deliver the cures of tomorrow to the people who need them today.
The Data Scientist will query and analyze large, complex datasets to generate insights that support revenue cycle optimization across an evolving cancer care delivery environment. This role applies advanced analytics, statistical modeling, and machine learning techniques to improve financial performance, streamline end\-to\-end revenue cycle operations, and enhance the patient financial experience. Working closely with revenue cycle leadership, clinical teams, and IT partners, this role translates data into actionable strategies that improve reimbursement, reduce denials, and optimize operational efficiency.
As a successful candidate, you will:
- Partner with revenue cycle, finance, and operational leaders to drive data\-informed decision making across billing, coding, and collections
- Analyze large datasets from EHR, billing, claims, and financial systems to identify revenue leakage and optimization opportunities
- Develop and deploy predictive models to improve denials management, cash collections, and reimbursement outcomes
- Design and implement pilots to test and optimize revenue cycle strategies in real\-time environments
- Build dashboards and visualizations to monitor KPIs such as AR days, denial rates, and net revenue performance
- Translate complex data findings into actionable insights for operational and executive stakeholders
- Identify trends and patterns related to payer performance, coding accuracy, and claim adjudication
- Lead analytics projects that support revenue cycle transformation and process improvement initiatives
- Collaborate cross\-functionally with IT and clinical teams to enhance data quality and reporting capabilities
Your qualifications should include:
- Master’s degree with 0\+ years of experience, or Bachelor’s degree with 3\+ years of relevant experience
- Experience with data analytics, statistical modeling, and machine learning techniques
- Proficiency in at least one programming language such as Python, R, Java, or C/C\+\+
- Strong experience with SQL and working with relational databases (e.g., Snowflake), along with data visualization tools such as Tableau
- Experience with machine learning methods (e.g., Random Forest, XGBoost, LightGBM) and deep learning techniques (e.g., CNN, RNN, LSTM)
- Demonstrated ability to extract, analyze, and interpret data to develop predictive models and drive financial outcomes
- Experience creating data visualizations and communicating insights to non\-technical audiences
- Strong problem\-solving skills with the ability to manage complex, cross\-functional projects
- Familiarity with healthcare revenue cycle data, EHR systems (EPIC), claims, billing, and payer workflows required
- Experience with cloud platforms such as AWS or Azure and modern data science tools preferred
City of Hope employees pay is based on the following criteria: work experience, qualifications, and work location.
City of Hope is an equal opportunity employer.
To learn more about our Comprehensive Benefits, pleaseCLICK HERE.
Salary Context
This $110K-$176K range is below the median 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 City of Hope, 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. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($143K) sits 28% below the category median. Disclosed range: $110K to $176K.
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.
City of Hope AI Hiring
City of Hope has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $176K - $176K.
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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