Data Analyst - Kisumu at Kenya Medical Research - KEMRI

Key Responsibilities:

• Develop rigorous analysis strategies for abstracts, manuscripts, and program evaluation activities. Authorship as part of these analysis activities.
• Apply complex statistical techniques and methods in the processing and analysis of data
• Directly supervise and mentor the Statistician
• Develop and implement program tracking databases in support of program evaluation activities
• Organize and direct the study design, data collection, processing, analysis and publication of statistical data on various subject matter relevant to the YIA Oncology study
• Support investigators to operationalize research goals, refine statistical hypotheses, develop statistical analysis plans and explain statistical implications of results.
• Merge data across databases and check for data inconsistencies and outliers using STATA
• Clean and edit complex medical record data for analysis
• Generate data reports on program activities, both routine and as requested
• Document methodologies and procedures used in the compilation and analysis of data, as well as data sources and limitations of estimates and guidelines for their use
• Provide advice on sample size calculation
• Provide advice on the statistical interpretation and implications of results for program planning and decision-making
• Prepare tables, figures, results, and statistical methods narratives for abstracts and publications

Vacancy Requirements:

• Strong academic qualifications in Biostatistics/Statistics or Applied Mathematics as evidenced by possession of at least a Degree from a recognized University
• At least 4 years’ experience in statistical work at the professional level, preferably in a healthcare setting  
• Advance level knowledge of statistical software: SAS and/or STATA data management is required  
• In-depth, comprehensive, and evolving knowledge of statistical and mathematical analysis techniques integrated with computer applications
• Demonstrated expertise in the following techniques is required: logistic and linear regression, mixed model, longitudinal data analyses, interrupted time series analyses, factor analyses, complex survey sample analyses, survival analyses, missing imputation, bootstrapping  
• Fluency in English