Data Analytics Scientist - Internal Medicine (Medical Informatics)

University of Kansas Medical Center•Kansas City, KS

5d•Onsite

About The Position

The Data Analytics Scientist in the Division of Medical Informatics provides advanced analytical and technical support for biomedical, clinical, translational, and population health research. The position collaborates with faculty investigators, clinicians, biostatisticians, informaticians, and other stakeholders to translate research questions into data-driven analytical strategies using electronic health records (EHR), claims, registries, and other real-world data sources. Responsibilities include data acquisition and preparation, cohort development, statistical and computational analysis, predictive modeling, data interpretation, and development of reproducible analytical workflows. The position contributes to research study development, manuscripts, grant proposals, presentations, and other scholarly products while applying appropriate data management, quality, security, and informatics practices.

Requirements

Bachelor's degree in computer science, biomedical informatics, data science, statistics, biostatistics, mathematics, engineering, epidemiology, or a related quantitative discipline.
Five (5) years of experience developing, testing, debugging, documenting, and maintaining analytical software or data-processing applications.
Experience using SQL and at least one general-purpose programming language, such as Python, R, or SAS.
Experience with relational databases, data modeling, data warehousing, or large-scale data processing.
Experience developing ETL/ELT workflows and integrating data from multiple sources.
Experience applying software development practices, including version control, testing, code review, and technical documentation.
Experience troubleshooting technical problems and developing software solutions.
Experience using generative AI tools, including large language models or AI-assisted coding and research tools, in software development, data analysis, documentation, or research.

Nice To Haves

Master's or doctoral degree in computer science, biomedical informatics, data science, statistics, biostatistics, engineering, or a related discipline.
Professional experience in data science, data engineering, software engineering, biomedical informatics, or a closely related technical field.
Experience developing and maintaining software applications, data pipelines, or analytical solutions using Python and SQL.
Experience working with large-scale databases or data-processing environments.
Experience working with healthcare, clinical, biomedical, or research data.
Experience applying software development practices, including version control, testing, documentation, and code review.

Responsibilities

Serve as a research associate and scientific collaborator on biomedical informatics, clinical, translational, population health, and real-world data research projects.
Collaborate with faculty investigators, clinicians, biostatisticians, informaticians, and other stakeholders to formulate research questions, hypotheses, study aims, and analytical strategies.
Contribute to the design and execution of observational studies, comparative effectiveness research, health services research, clinical research, and other data-driven investigations.
Translate scientific questions into computable phenotypes, cohort definitions, data specifications, outcome definitions, and reproducible analytical workflows.
Conduct literature reviews, feasibility assessments, preliminary analyses, and data characterization to support study development and determine the suitability of available data resources.
Collect, preprocess, integrate, clean, transform, and analyze large-scale structured and unstructured healthcare data, including EHR, claims, registry, research, and other real-world data.
Develop and apply statistical models, machine-learning algorithms, natural language processing, predictive models, and other computational methods to address biomedical and clinical research questions.
Interpret analytical findings, evaluate robustness and limitations, and communicate results effectively to investigators and both technical and non-technical stakeholders.
Contribute to scientific manuscripts, abstracts, conference presentations, technical reports, grant proposals, research protocols, statistical analysis plans, and other scholarly products.
Design, develop, optimize, and maintain scalable relational databases, research data repositories, data warehouses, data marts, and associated database schemas and data models.
Design and maintain robust ETL/ELT pipelines for extracting, integrating, harmonizing, validating, and loading large-scale healthcare datasets from heterogeneous sources.
Optimize SQL queries, database structures, indexing strategies, storage approaches, and data-processing workflows to improve performance, scalability, reliability, and maintainability.
Design, develop, test, deploy, document, and maintain production-quality software applications, APIs, analytical packages, data pipelines, and automation tools using Python, R, SQL, SAS, and other appropriate technologies.
Apply modern software engineering practices, including Git-based version control, modular design, peer code review, automated testing, CI/CD, containerization, documentation, and release management.
Participate across the software development lifecycle (SDLC) and translate research and operational requirements into scalable, secure, maintainable, and cost-effective technical solutions.
Support healthcare data interoperability and harmonization using common data models, standards, terminologies, and ontologies such as PCORnet CDM, OMOP, FHIR, HL7, UMLS, LOINC, SNOMED CT, and RxNorm, as applicable.
Implement and maintain data quality, validation, provenance, lineage, metadata, reproducibility, and change-management processes for research data and analytical products.
Ensure appropriate data security, privacy, governance, and regulatory compliance, working with investigators, IRB personnel, information security teams, and other stakeholders on HIPAA, data-use, and research requirements.
Provide technical leadership and mentorship to analysts, students, research staff, and junior technical team members, and contribute to shared standards for data architecture, software development, analytics, and research reproducibility.
Manage and contribute to the full lifecycle of multidisciplinary research and informatics projects, including planning, requirements gathering, technical design, implementation, monitoring, documentation, scientific dissemination, and continuous improvement.

Benefits

Health insurance
Dental insurance
Vision insurance
Health expense accounts with generous employer contributions
Employer-paid life insurance
Long-term disability insurance
Various additional voluntary insurance plans
Paid time off, including vacation and sick
Ten paid holidays
One paid discretionary day after six months of employment
Paid time off for bereavement, jury duty, military service, and parental leave after 12 months of employment
Retirement program with a generous employer contribution
Additional voluntary retirement programs (457 or 403b)