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Social & Scientific Systems

Silver Spring, United States

Application Deadline

Social & Scientific Systems (SSS), a DLH Corporation company, serves federal government clients throughout the United States and abroad delivering technology enabled solutions in key health and human services programs. The Company's core competencies include secure data analytics and statistics, clinical trials and laboratory services, a full suite of public health research offerings, performance evaluation, system modernization, operational logistics and readiness, and strategic digital communications. DLH has over 2,000 employees serving numerous government agencies.

SSS is currently seeking an Epidemiologist or Biostatistician to serve on a multi?disciplinary team that conducts clinical data analyses for reporting and high level publication using medical claims and/or electronic health record data on behalf of the National Institute of Diabetes and Digestive and Kidney Diseases, and other federal agencies. We are dedicated to maintaining a cutting edge research portfolio of compendia and manuscripts on diabetes, digestive, kidney and urologic diseases that informs and advances “real world” clinical practice. You will be the lead of a collegial team that includes epidemiologists, statisticians, data managers and programmers, and operations staff responsible for working with NIDDK clinician researchers and statisticians in the design, development, execution, and publication of epidemiologic research studies on kidney, urological, or hematological diseases. The position offers many opportunities for leadership, professional development, innovation, collaboration across disciplines, and authorship at multiple levels.

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily:

  • Collaborate with colleagues and clients to design, implement, and publish novel studies in the area of kidney, urological, and hematological disease.
  • Present analytical results to clients in biweekly meetings. Participate in collegial discussions to determine significance, gaps, and validity of analytical data towards the delivery of a final manuscript or report.
  • Apply epidemiologic methods to design and execute statistical analyses under minimal guidance.
  • Prepare oral and written scientific communications, including scientific presentations, manuscripts, and technical reports.
  • Provide guidance to data and statistical programming staff and junior analysts.
  • Perform additional duties as required.

Candidates for this position should have the following education/experience and demonstrate mastery of the following knowledge, skills, and abilities:

Doctoral degree in epidemiology, biostatistics, or related field (or Masters degree with at least 5 years of experience).

Must have experience in the design and execution of observational epidemiologic studies and be competent in following areas:

  • Extract, join, and clean large volumes of structured “real world” data from medical claims and electronic health records archived in relational databases;
  • Execution of advanced statistical analysis, such as for time to event, repeated measures, and nested case-control designs.
  • Designing and implementing analytic solutions to account for various forms of bias or other features of the data to improve inference (competing risks, time varying confounding, confounding by indication, selecting bias, missing data)
  • Aptitude to learn new methods as needed.
  • Track record of peer-reviewed publications. Able to efficiently communicate research findings in oral presentations and written manuscripts with data visualization through use of figures and tables.
  • Intellectual curiosity for clinical kidney, urological, and hematologic diseases.
  • Data inquisitiveness and naturally inclined to analytically go deeper into “big data” sets to uncover data gaps and limitations.
  • Requires proficiency in SAS; additional proficiencies in other analytics packages (R, Python, Stata, etc.) would be an asset.
  • Able to work well in a team environment and independently, and to learn new skills as needed.
  • Experience with analysis of unstructured data would be an asset (natural language processing, machine learning and other artificial intelligence methods, text mining).


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