Data Scientist Cancer Neuroscience

Support cancer neuroscience research by building reliable data infrastructure, analytical workflows, and predictive modeling capabilities for CNP projects.

Responsibilities

• Create and maintain a structured, up-to-date 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.
• Document institutional data asset attributes, including dataset ownership, access requirements, data dictionaries, cohort definitions, refresh cadence, data limitations, analytical readiness, and potential research use cases.
• Act 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 stakeholders to improve awareness, responsible use, and strategic alignment.
• Find opportunities to harmonize, link, reuse, and scale CNP-aligned datasets while reducing duplicative data collection and supporting governance, documentation, and interoperability standards.
• Help CNP leadership use inventory and analytical outputs for project prioritization, infrastructure planning, collaboration opportunities, grant development, philanthropy reporting, and long-range strategic planning.
• Design, build, maintain, and document data pipelines for prospective and retrospective oncology cohorts, including clinical, research, operational, and multimodal datasets.
• Collect, clean, harmonize, preprocess, and validate data from multiple sources to support 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 support efficient data access, analysis, reporting, and collaboration across CNP-supported projects.
• Work with large datasets using Python, R, SQL, Spark, Foundry, Microsoft Fabric, AWS, Azure, GCP, or comparable environments.
• Conduct 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 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 platforms, including best practices for access, workflow development, documentation, and collaboration.
• Assist users with analytical question definition, technical issue resolution, workflow improvements, 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 priorities.
• Communicate findings via visualizations, dashboards, reports, presentations, and clear written summaries for technical and non-technical audiences.
• Collaborate on scientific publications, abstracts, grant applications, technical reports, and other documentation for faculty, leadership, advisory boards, philanthropy, and project teams.
• Prepare technical reports and other documentation for upper management and team members for short- and long-range projects and planning.
• Contribute to institutional data integration by aligning 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 data science, machine learning, artificial intelligence, clinical informatics, and big data technologies.
• Other duties as assigned.

Requirements

• Bachelor's degree in Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Science, Engineering, Computer Science, Statistics, Computational Biology, or related field.
• Three years of scientific software or industry development/analysis experience.
• With a Master's degree: one year required experience.
• With a PhD: no experience required.

Technologies

• Python, R, SQL, Spark
• Foundry
• Microsoft Fabric
• AWS, Azure, GCP

Benefits

• Medical
• Dental
• Paid time off
• Retirement
• Tuition benefits
• Educational opportunities
• Individual and team recognition

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.

Education

• 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.

Additional Information

• Requisition ID: 183428
• Employment Status: Full-Time
• Employee Status: Regular
• Work Week: Days
• Work Location: Houston, TX (onsite)
• Work Location: Hybrid Onsite/Remote
• Pivotal Position: Yes
• Referral Bonus Available?: Yes
• Relocation Assistance Available?: Yes
• FLSA: Exempt and not eligible for overtime pay
• Fund Type: Soft
• Salary: USD 106,500 - 159,500 per year
• Minimum Salary: USD 106,500
• Midpoint Salary: USD 133,000
• Maximum Salary: USD 159,500
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