Research Specialist Intermediate

UVA Health•Charlottesville, VA

13d•Onsite

About The Position

This role plays a key part in advancing cancer-related population health research within the Population Health and Cancer Outcomes Core (PHCOC). The specialist will support the Core Director and Lead Statistician in producing analytical reports on cancer incidence, mortality, treatment patterns, and outcomes using large-scale state and national databases. These reports will inform clinical practice, population health strategy, and public health policy. The position is responsible for the full data lifecycle, from acquisition and preprocessing to statistical modeling, visualization, and reporting. Additionally, the role involves teaching and sharing analytical knowledge and best practices with the broader university research community.

Requirements

Bachelor's degree in statistics, biostatistics, data science, economics, public policy or a closely related quantitative field along with a minimum of 3 years of relevant quantitative data modelling experience.
OR Master's degree in statistics, biostatistics, data science, economics, public policy or a closely related quantitative field will be accepted in lieu of experience.
Demonstrate knowledge of data management, data cleaning, and preprocessing to conduct rigorous statistical analysis and interpretation.
Demonstrate knowledge of advanced statistical methodologies, including linear and logistic regression, longitudinal data analysis, and econometric modeling.
Demonstrate knowledge of statistical programming for large datasets using software such as SAS, Stata, R, and Python.
Demonstrate knowledge of extracting and querying large-scale datasets using SQL in both local and cloud environments.
Demonstrate knowledge of applied machine learning and artificial intelligence tools to identify predictive factors and improve analytic performance.
Demonstrate knowledge of batch processing and data science workflows for efficient handling of largescale datasets.
Demonstrate knowledge of working with community-level and population-based data sources, including the American Community Survey (ACS).
Demonstrate knowledge of working within virtual machine and cloud-based environments for data processing and analysis.
Demonstrate knowledge of spatial data analysis using ArcGIS or related GIS tools.
Demonstrate knowledge of creating data visualizations, dashboards, and infographics using software such as Tableau, R, or Python.
Demonstrate knowledge of developing standardized analytical workflows, reproducible data pipelines, technical documentation and using version control tools such as Git.
Demonstrate knowledge of managing multiple projects simultaneously, prioritizing tasks efficiently, and tracking progress.
Demonstrate knowledge of teaching lectures or workshops focused on data programming and analytics.

Responsibilities

Play a key role in advancing cancer-related population health research within the Population Health and Cancer Outcomes Core (PHCOC).
Support the Core Director and Lead Statistician in producing analytical reports on cancer incidence, mortality, treatment patterns and outcomes using large scale state and national databases to inform clinical practice, population health strategy and public health policy.
Responsible for full data lifecycle – from data acquisition and preprocessing to statistical modeling, visualization and reporting of results.
Teach and share analytical knowledge and best practices with the broader university research community.
Extract and clean data from large-scale cancer-related administrative and clinical databases, including SEER-Medicare, state cancer registries, the Virginia All-Payer Claims Database (APCD), and Electronic Health Record (EHR) data.
Generate analytical patient cohorts using SQL in virtual machine and cloud computing environments.
Conduct advanced statistical analyses, including linear and logistic regression, survival analysis, Cox proportional hazards modeling, propensity score matching, Blinder–Oaxaca decomposition, longitudinal data analysis, and econometric modeling to examine cancer outcomes and disparities in cancer care.
Utilize statistical software packages such as SAS, Stata, R, and Python to perform data analysis.
Apply machine learning and artificial intelligence tools to identify predictive factors and optimize analytic performance.
Assist in the preparation of administrative and compliance documents required for database access and data use agreements.
Perform geospatial analyses to assess geographic variation in cancer burden, healthcare access, and outcomes, using tools such as ArcGIS.
Create data visualizations, dashboards, and infographics to communicate findings, using tools such as Tableau, Canva, Lucidchart, and Microsoft PowerPoint.
Prepare publication-ready written and graphical outputs for manuscripts, reports, and conference presentations.
Collaborate with faculty, clinicians, and researchers to interpret statistical findings and translate analytical results into actionable insights for research and policy.
Contribute to the development and implementation of standardized analytic workflows and reproducible data pipelines for quality assurance and reproducibility.
Lead teaching workshops throughout the year to train students and staff in working with healthcare claims and population datasets.