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About This Role
### Working at Moffitt is both a career and a mission: to contribute to the prevention and cure of cancer.
### As the only National Cancer Institute\-designated Comprehensive Cancer Center based in Florida, Moffitt employs some of the best and brightest minds from around the world. Join a dedicated team of nearly 11,000 who are shaping the future we envision. Moffitt has been recognized as a Best and Brightest Company to Work For in the Nation and is continually named one of the Tampa Bay Times’ Top Workplaces.
Summary
The AI Data Scientist \- Precision Oncology develops and applies advanced analytics, machine learning, and artificial intelligence methodologies to support precision oncology research and AI\-enabled clinical outcome optimization initiatives. This role works with large\-scale clinical, imaging, pathology, molecular, genomic, and outcomes datasets to generate actionable insights, develop predictive models, and support the evaluation and implementation of AI technologies across cancer research and healthcare applications. The AI Data Scientist works at the intersection of data science, machine learning, biomedical data science, and cancer research.This position collaborates closely with other AI Data Scientists, AI/ML Engineers, clinicians, and informaticians to develop high\-quality datasets, conduct statistical and predictive analyses, evaluate AI models, and support institutionally scalable oncology AI initiatives.Other related duties as assigned by appropriate Leadership. Job Title: AI Data Scientist Precision Oncology
Minimum Education Level: Bachelor's Degree
Minimum Field of Study: Computer Science, Data Science, Statistics, Biostatistics, Mathematics, Bioinformatics, Biomedical Engineering, Computer Engineering, Electrical Engineering, Informatics, or a related quantitative discipline
Minimum Education Notes:
Preferred Education Level: Master's Degree
Preferred Field of Study: Computer Science, Data Science, Statistics, Biostatistics, Mathematics, Bioinformatics, Biomedical Engineering, Computer Engineering, Electrical Engineering, Informatics, or a related quantitative discipline
Preferred Education Notes:
Licensure Certification Needed: No
Minimum Licensure:
Minimum Licensure (Other):
Training Required: • Strong programming skills in Python.• Experience with data analysis, statistical modeling, and machine learning techniques.• Experience using data science libraries such as Pandas, NumPy, Scikit\-learn, or equivalent tools.• Experience working with structured and unstructured datasets.• Experience with SQL and database technologies.• Experience performing data cleaning, preprocessing, feature engineering, and quality assessment.• Experience creating analytical reports, visualizations, and dashboards.• Experience working in Linux\-based analytical environments.• Experience using Git and collaborative software development tools.• Experience using modern AI\-assisted coding and analytical tools.• Experience conducting exploratory data analysis, hypothesis testing, statistical inference, and outcomes\-focused analytical studies.
Salary Range
$77,022\.40 \- $117,582\.40*Salary ranges posted for this position represent the expected base pay range for the role. Actual compensation may vary based on location and a variety of job\-related factors, including experience, skills, education, and internal equity among Team Members in similar positions.*
*We are committed to maintaining fair and equitable pay practices and regularly review compensation to ensure alignment across our workforce.*
Moffitt Career Site
*If you have the vision, passion, and dedication to contribute to our mission,*
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*then we have a place for you!*
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1\. Equal Employment Opportunity
Moffitt Cancer Center is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, or protected veteran or disabled status. We seek candidates whose skills, and personal and professional experience, have prepared them to contribute to our commitment to diversity and excellence.
2\. Reasonable Accommodation
Federal law requires employers to provide reasonable accommodation to qualified individuals with disabilities. Please tell us if you require a reasonable accommodation to apply for a job or to perform your job. Examples of reasonable accommodation include making a change to the application process or work procedures, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment. Moffitt endeavors to make moffitt.org/careers accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact one of the Human Resources receptionists by phone at 813\-745\-7899 or by email at [email protected]. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications.
Read more information about your EEO rights under the law.
Transparency in Coverage Rule
Salary Context
This $77K-$117K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Moffitt Cancer Center, 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 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($97K) sits 50% below the category median. Disclosed range: $77K to $117K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Moffitt Cancer Center AI Hiring
Moffitt Cancer Center has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Tampa, FL, US. Compensation range: $117K - $201K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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