Supervisory Data Scientist

The Supervisory Data Scientist serves as the Precision Medicine Bioinformatics Lead within the Veterans Health Administration's National Oncology Program. The position directs national precision medicine data initiatives, oversees genomic data integration and analytics, develops strategies supporting clinical decision-making and research, and supervises professional staff to improve the quality, effectiveness, and delivery of Veteran healthcare services. Basic Requirements: United States Citizenship: Non-citizens may only be appointed when it is not possible to recruit qualified citizens in accordance with VA Policy. Individual Occupational Requirements (IOR): The 1560 Occupational series has an IOR that must be met: Degree: Mathematics, statistics, computer science, data science or field directly related to the position. The degree must be in a major field of study (at least at the baccalaureate level) that is appropriate for the position. (transcripts required) -OR- Combination of education and experience: Courses equivalent to a major field of study (30 semester hours) as shown in paragraph A above, plus additional education or appropriate experience (transcripts required). Specialized Experience: You must have one year of specialized experience equivalent to at least the next lower grade GS-13 in the normal line of progression for the occupation in the organization. Examples of specialized experience would typically include, but are not limited to: Applying advanced bioinformatics and computational methods to analyze genomic, molecular, or clinical datasets, including next-generation sequencing (NGS) data processing, variant interpretation, and development or application of analytical algorithms. Developing and implementing complex data management and analytical solutions using programming languages such as Python, R, Perl, or Java; relational databases (SQL); and data extraction, transformation, and loading (ETL) processes to integrate and analyze large-scale genomic or clinical datasets. Applying statistical analysis, machine learning, or super/artificial intelligence techniques to evaluate genomic or clinical data, identify biomarkers, correlations, or patterns, and develop analytical findings or visualization tools supporting clinical decision-making or research. Leading complex data science projects and evaluating program effectiveness through strategic planning, quantitative and qualitative analyses, coordination with multidisciplinary stakeholders, and development of technical recommendations to improve data management processes or organizational operations. Physical Requirements: Work is primarily sedentary, although frequent site and field visits may require some minor physical demand.