About the Role

The Associate, Data and Statistics supports the cleaning, management, analysis, and documentation of research and operational data. The role ensures that datasets are accurate, organized, reproducible, and analysis ready. The Associate contributes to high quality statistical outputs that inform publications, internal learning, dashboards, and cross functional decision making.

Shamiri conducts randomized trials, pilots, longitudinal studies, implementation evaluations, and operational performance tracking. As the organization scales, the volume and complexity of data increases. Reliable data systems and structured analytic workflows are essential to maintain scientific rigor and enable continuous improvement.

The Associate, Data and Statistics strengthens the research and learning infrastructure by supporting data cleaning and validation processes, assisting in statistical analyses, maintaining reproducible scripts and documentation, producing clear tables and figures, and ensuring structured data storage and version control. This role supports the Senior Associate and broader research team in transforming raw data into reliable insights.

Roles and Responsibilities

Data cleaning and quality assurance

• Clean, organize, and prepare datasets using reproducible code in R.
• Conduct validation checks, including range checks, missingness analysis, duplicate detection, and consistency verification.
• Support development and maintenance of codebooks, data dictionaries, and metadata documentation.
• Identify and flag anomalies or inconsistencies in datasets.
• Collaborate with research management teams to resolve field data issues.

Statistical analysis support

• Conduct descriptive statistics and exploratory data analysis.
• Support implementation of regression analyses and other inferential models.
• Prepare cleaned datasets for advanced modeling by senior staff.
• Assist with execution of pre registered analysis plans.
• Conduct preliminary robustness checks and sensitivity analyses as assigned.

Reproducibility and documentation

• Write clean, well commented, and reproducible scripts.
• Maintain organized folder structures and version control of scripts and outputs.
• Document analytic decisions, variable transformations, and data cleaning steps.
• Ensure that analytic workflows can be replicated by other team members.

Tables, figures, and reporting

• Prepare summary statistics and charts for donor reports and internal briefs.
• Support preparation of statistical sections of manuscripts and reports.
• Collaborate with the Knowledge team to ensure statistical accuracy in written outputs.

Database and data systems support

• Assist in maintaining structured databases and data repositories.
• Contribute to data quality checks within database systems.
• Support integration of data from multiple platforms such as survey tools, operational systems, and dashboards.

Cross functional collaboration

• Respond to internal data requests from delivery, clinics, technology, and product teams.
• Present findings in internal meetings when appropriate.
• Collaborate to align analytic outputs with research and operational priorities.

Capacity building

• Support training of junior staff on data quality and coding standards.
• Participate in internal workshops on analytics, reproducibility, and statistical literacy.

Key competencies

• Proficiency in R for data cleaning and analysis.
• Working knowledge of SQL for querying structured databases.
• Strong Excel skills for quality assurance and structured analysis tasks.
• Solid understanding of quantitative and qualitative research methods and study design.
• Strong data cleaning and wrangling skills.
• Attention to detail and commitment to data quality.
• Clear documentation discipline and reproducibility mindset.
• Ability to communicate analytic findings clearly and concisely.
• Professionalism and strong ethical standards in data handling.

Qualifications

• Bachelor’s or Master’s degree in statistics, data science, psychology, economics, public health, or a related quantitative field.
• 1 to 3 years of experience in data analysis, research support, or statistical work.
• Demonstrated experience cleaning and analyzing datasets using R.
• Experience using SQL is preferred.
• Experience working with research or evaluation data, including survey or experimental data, is preferred.
• Familiarity with database systems such as MySQL or similar platforms is an advantage.