Data Analyst Job in Kenya

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

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