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About This Role
The University of Texas MD Anderson Cancer Center is home to the Institute for Data Science in Oncology, a multidisciplinary program focused on applying data science and emerging technologies to improve safety, quality, and access in clinical care and operations. Within the Institute's surgical data science program, the Data Scientist \- Operations Research supports translational research in image\-guided surgery and collaborates closely with data scientists, engineers, clinicians, and scientific partners to advance innovative approaches in surgical care.
UT MD Anderson is a leading institution focused on cancer care, research, education, and prevention. The Data Scientist \- Operations Research contributes to the development and clinical translation of advanced imaging, image analysis, and registration methods. The Data Scientist \- Operations Research works with surgeons and collaborators across the institution, with primary areas of impact in orthopaedic surgery and neurosurgery. The Data Scientist \- Operations Research plays a key role in advancing computational approaches that support data\-driven surgical care.
The ideal candidate has advanced education in science, engineering, computer science, statistics, computational biology, or a related field, with preference given to candidates holding a Master's or PhD degree. Preferred experience includes clinical operations and workflow analysis in surgery, radiology, or other clinical specialties, along with knowledge of patient safety factors, systems engineering, systems integration, requirements management, risk mitigation, programming in Python and/or Matlab, statistical analysis, and advanced data science techniques.
Why Us?
Join UT MD Anderson and contribute to transformative research that improves patient care, safety, quality, and operational efficiency. This role offers opportunities to collaborate with leading clinicians, engineers, and researchers while advancing innovative surgical data science initiatives. Team members benefit from meaningful work, professional development opportunities, and a comprehensive benefits package that supports long\-term career growth and personal well\-being.
- Employer\-paid medical coverage starting day one for employees working 30\+ hours/week, plus optional group dental, vision, life, AD\&D, and disability insurance.
- Accruals for PTO and Extended Illness Bank, plus paid holidays, wellness, childcare, and other leave options.
- Tuition Assistance Program after six months of service and access to extensive wellness, fitness, and employee resource groups.
- Defined\-benefit pension through the Teachers Retirement System, voluntary retirement plans, and employer\-paid life and reduced salary protection programs.
Responsibilities
Clinical \& Research Collaboration
- Collaborate with clinicians, engineers, data scientists, and scientific partners on surgical data science initiatives
- Work closely with surgeons and collaborators in orthopaedic surgery, neurosurgery, and related clinical specialties
- Assist supervisors and senior research staff with reports, grant requests, and research activities
- Provide consulting support to internal teams and external collaborators regarding data models, findings, and process improvements
Research \& Experimental Design
- Plan and execute experiments and evaluate, interpret, and communicate study results
- Participate in the development and revision of research techniques and methodologies
- Assemble, operate, and support laboratory apparatus and equipment used in research activities
- Develop computational modeling approaches related to surgical data science
Data Acquisition \& Clinical Systems
- Work with clinical hardware and software, including anesthesia systems, imaging platforms, surgical robots, and related technologies
- Capture and curate multidimensional data from healthcare environments
- Utilize healthcare information systems including electronic medical records, EPIC, PACS, and related clinical data sources
- Support development of new data science methods in surgery
Data Engineering \& Analytics
- Create data tools for analytics and data science team members
- Design and maintain optimal data pipeline architecture
- Build systems for extraction, transformation, and loading (ETL) of data from diverse clinical data sources
- Develop analytics tools that generate actionable insights related to clinical workflow and operational efficiency
Leadership \& Oversight
- Supervise technicians and provide guidance on assigned work activities
- Support requirements specification, management, and risk mitigation activities
- Participate in workflow analysis, process mapping, and systems integration efforts
- Perform other duties as assigned
OTHER REQUIREMENTS
- Required: Demonstrated experience with systems engineering, systems integration, workflow analysis, process mapping, human factors engineering, clinical data analysis, specification and management of requirements, risk management / mitigation, unsupervised learning, decision trees, A/B testing, and natural language processing
- Preferred: Knowledge and experience with computing / programming (Python and/or Matlab), statistical analysis, experience with clinical operations and workflow in surgery, radiology, or other areas of clinical medicine, and experience with factors affecting patient safety
EDUCATION
- Required: Bachelor's Degree Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Science, Engineering, Computer Science, Statistics, Computational Biology, or related field.
- Preferred: Master's Degree Science, Engineering or related field.
- Preferred: PhD Science, Engineering or related field.
WORK EXPERIENCE
- Required: 3 years Scientific software or industry development/analysis experience. or
- Required: 1 year Required experience with Master's degree. or
- Required: With PhD, no experience required.
The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113\.001(2\) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state, or local laws unless such distinction is required by law.http://www.mdanderson.org/about\-us/legal\-and\-policy/legal\-statements/eeo\-affirmative\-action.html
Additional Information
- Requisition ID: 182325
- Employment Status: Full\-Time
- Employee Status: Regular
- Work Week: Days
- Minimum Salary: US Dollar (USD) 106,500
- Midpoint Salary: US Dollar (USD) 133,000
- Maximum Salary : US Dollar (USD) 159,500
- FLSA: exempt and not eligible for overtime pay
- Fund Type: Soft
- Work Location: Hybrid Onsite/Remote
- Pivotal Position: Yes
- Referral Bonus Available?: Yes
- Relocation Assistance Available?: Yes
\#LI\-Hybrid
Salary Context
This $106K-$159K range is below the median 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 MD Anderson 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 ($133K) sits 31% below the category median. Disclosed range: $106K to $159K.
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.
MD Anderson Cancer Center AI Hiring
MD Anderson Cancer Center has 1 open AI role right now. They're hiring across Data Scientist. Based in Houston, TX, US. Compensation range: $159K - $159K.
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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