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About This Role
The Cancer Neuroscience Program (CNP) Data Scientist role will serve as a key technical and analytical resource supporting the development, implementation, and maintenance of research data infrastructure, analytical workflows, and data-driven tools for the CNP. As an institutional signature priority, CNP brings together multidisciplinary efforts focused on the intersection of cancer, the nervous system, brain health, and neuro-oncology.
Reporting to the Director, Research Planning & Development, this position will support prospective and retrospective oncology cohorts; design and maintain transparent, reproducible, and auditable data pipelines; enable data access and workflow development in platforms such as Foundry and related institutional data environments; perform exploratory and advanced analytics; develop predictive and prescriptive models; and translate complex data into actionable insights for scientific, operational, and strategic decision-making.
The role will also help create and maintain visibility across neuro-focused datasets, cohorts, and data assets across the institution, serving as a technical connector between CNP-supported investigators, institutional research data resources, the Institute for Data Science in Oncology, the institutional Research Data Office, and other enterprise data stakeholders. This function is intended to improve awareness, responsible access, reuse, harmonization, and strategic alignment of data assets relevant to cancer neuroscience research.
The ideal candidate is a technically strong, research-oriented data scientist who can operate effectively in a matrixed academic healthcare environment, communicate clearly with technical and non-technical stakeholders, and contribute directly to cancer neuroscience research, publications, reporting, and long-range program planning. The candidate should be intellectually curious, service-oriented, highly organized, and capable of supporting multiple concurrent projects in a fast-moving, multidisciplinary research environment.
JOB FUNCTIONS:
- Create and maintain a structured, current inventory of neuro-focused datasets, cohorts, registries, biospecimen-linked datasets, clinical data assets, imaging datasets, patient-reported outcomes, molecular datasets, and other relevant research resources across the institution.
- Document key attributes of institutional data assets, including dataset ownership, access requirements, data dictionaries, cohort definitions, refresh cadence, data limitations, analytical readiness, and potential research use cases.
- Serve as a technical connector between CNP-supported investigators, neuro-focused research initiatives, institutional data teams, the Institute for Data Science in Oncology, the institutional Research Data Office, and other enterprise data stakeholders to improve awareness, responsible use, and strategic alignment of data assets.
- Identify opportunities to harmonize, link, reuse, and scale existing datasets across CNP-aligned projects while reducing duplicative data collection and supporting institutional standards for data governance, documentation, and interoperability.
- Support CNP leadership in using data asset inventories and analytical outputs to inform project prioritization, infrastructure planning, collaboration opportunities, grant development, philanthropy reporting, and long-range strategic planning.
- Design, build, maintain, and document data pipelines supporting prospective and retrospective oncology cohorts, including clinical, research, operational, and multimodal datasets.
- Collect, clean, harmonize, preprocess, and validate data from multiple sources to ensure accuracy, completeness, usability, and reproducibility.
- Develop and maintain transparent, auditable, and well-commented code bases for routine and advanced data science tasks.
- Create reusable workflows, templates, and documentation that enable efficient data access, analysis, reporting, and collaboration across CNP-supported projects.
- Work with large datasets using coding languages, analytical tools, and data platforms such as Python, R, SQL, Spark, Foundry, Microsoft Fabric, AWS, Azure, GCP, or comparable environments.
- Perform exploratory data analysis to identify trends, patterns, anomalies, missingness, bias, and potential signals relevant to cancer neuroscience research.
- Develop, test, validate, and refine predictive and prescriptive models using statistical methods, machine learning, artificial intelligence, and other advanced analytical techniques.
- Design and implement analytical experiments to validate hypotheses, evaluate model performance, and support scientific decision-making.
- Provide onboarding, hands-on training, and technical support to research staff and project teams using Foundry and related institutional data platforms, including best practices for data access, workflow development, documentation, and collaboration.
- Assist users in defining analytical questions, resolving technical issues, improving workflows, and translating research needs into feasible data science approaches.
- Collaborate with clinicians, laboratory scientists, data scientists, data engineers, statisticians, bioinformaticians, research staff, and program leadership to optimize data insights and support CNP-aligned research priorities.
- Communicate findings effectively through visualizations, dashboards, reports, presentations, and clear written summaries tailored to technical and non-technical audiences.
- Collaborate on scientific publications, abstracts, grant applications, technical reports, and other documentation for use by faculty, leadership, advisory boards, philanthropy, and project teams.
- Prepare technical reports and other documentation for use by upper management and team members for short- and long-range projects and planning.
- Contribute to institutional data integration efforts by helping align CNP-specific data needs with broader IDSO-supported platforms, standards, and analytical capabilities.
- Ensure data governance, privacy, security, documentation, and compliance standards are followed in all data science activities.
- Stay current with emerging tools and techniques in data science, machine learning, artificial intelligence, clinical informatics, and big data technologies.
- Other duties as assigned.
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 or PhD in Science, Engineering, Data Science, Computer Science, Statistics, Biomedical Informatics, Computational Biology, Bioinformatics, Biostatistics, Public Health, or related field.
EXPERIENCE:
Required: Three years scientific software or industry development/analysis experience. With Master's degree, one year required experience. With PhD, no experience required.
Preferred: Experience in academic healthcare, oncology, cancer research, clinical research, translational research, or biomedical research. Experience working with EPIC-derived clinical data, electronic health record data, prospective or retrospective cohorts, clinical registries, institutional research datasets, real-world clinical data, or multimodal biomedical datasets. Experience with Foundry or similar enterprise data platforms. Experience developing analytical workflows, dashboards, technical reports, reusable data products, or data pipelines for research or operational stakeholders. Experience contributing to peer-reviewed publications, abstracts, grant applications, scientific presentations, philanthropy reports, or institutional strategy documents. Experience with machine learning, natural language processing, large language model-enabled workflows, or AI applications in healthcare or research settings. Experience working collaboratively with clinicians, scientists, statisticians, bioinformaticians, data engineers, research staff, and institutional data teams.
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: 183428
- 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: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% 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 $190,000 based on 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($133K) sits 30% below the category median. Disclosed range: $106K to $159K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
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, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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