Careers

Global Good

Statistician

The Institute for Disease Modeling shapes global efforts to eradicate infectious diseases and to achieve permanent improvements in the health of those most in need. By developing, using, and freely sharing computational modeling tools, IDM advises policymakers, promotes quantitative decision-making and advances scientific methodologies. IDM is a highly dynamic organization, composed of research scientists and software professionals, with a work environment that is defined by innovation and collaboration. As part of its work, IDM routinely collaborates with groups at the World Health Organization, the Center for Disease Control, PATH, the Bill and Melinda Gates Foundation, ministries of health in the developing world, as well as universities and research institutes.  IDM is an institute within the Global Good Fund, a collaboration between Intellectual Ventures and Bill and Melinda Gates.

IDM seeks a full-time Statistician to identify and lead or support research projects relevant to different aspects of the group’s analysis and modeling work (e.g. Polio, vaccine preventable diseases, health delivery), as well as to actively partner/support with other teams at IDM (e.g. MNCH, Health Economics, Measles, Epidemiology, Malaria, HIV/TB). Joining the group provides unique opportunities to interact with global health policy makers, to collaborate with world-class research laboratories and non-profit organizations, and to contribute to global and national disease eradication strategies.

The statistician will focus on diverse data and problems related to disease modeling and control strategies, analysis of risk factors, risk mapping, study design, and diagnosis/optimization of global health programs and delivery systems.  In collaborations with team members, external researchers, policymakers, and/or country health programs, the statistician may lead on solutions, support with analysis, or contribute subject-matter expertise on sound inferential practices and methods. The scientist will present to key stakeholders, at conferences, and prepare research articles.

The group seeks individuals with demonstrated achievements, a commitment to excellence, and a willingness to collaborate.

Responsibilities:

  • Flexibly engage in diverse policy and analysis questions that arise related disease control efforts and healthcare delivery
  • Provide subject matter expertise to internal team members and external collaborators on statistical methods and practice, e.g. sampling, model building, estimation, and interpretation
  • Support polio eradication efforts with analysis on risk and assessment of interventions
  • Gather, analyze, and model data about health gaps/burden, health interventions, and health delivery in countries of interest, e.g. maternal and child health
  • Write summaries of results to be used in policy recommendations, white papers, and scientific publications
  • Write research articles and conference presentations communicating projects and results to the scientific community

Key Qualifications and Required Skills:

  • PhD in Statistics, Biostatistics, or equivalent
  • Extensive knowledge of diverse statistical methods and application to data (applied statistics)
  • Proficiency in at least one data-analysis or scripting language (e.g. R, Python)
  • Knowledge of experimental design and sampling principles
  • Experience with various estimation paradigms/techniques, e.g. likelihood inference, Bayesian statistics
  • Ability to initiate, organize, and manage research projects and clearly communicate analysis results to diverse audiences
  • Demonstrated ability to work productively independently and as part of a team; work extended hours to meet a deadline
  • Knowledge of public health issues in developing world settings
  • Experience working with a software development team is a plus
  • Multiple peer-reviewed scientific articles

Desired Skills:

  • Familiarity with survey statistics, design-based inference
  • Experience with aspects of spatial statistics, disease mapping, and GIS
  • Knowledge of machine learning/statistical learning concepts and applications