Associate Data Analyst at Sproxil

Role Summary

• The Associate Data Analyst will support Sproxil’s Analytics & Data Intelligence team in turning large volumes of product verification, consumer engagement, supply chain, and public health data into actionable insights. Working directly under the Lead Data Analyst, the role holder will assist in the full data analysis lifecycle — from data acquisition and cleaning to dashboard development and reporting — across Sproxil’s diverse client programmes in pharmaceuticals, FMCG, cosmetics, agribusiness, and donor-funded public health initiatives.

Key Responsibilities

• Apply descriptive and inferential statistical methods to extract meaningful findings from surveys, and public health datasets.
• Conduct hypothesis testing, regression analysis, and significance testing to validate trends and support evidence-based decision-making.
• Collect, clean, validate, and organise data from multiple internal and external sources including Sproxil’s authentication platform, call centre systems, CRM, field surveys, and client datasets.
• Conduct exploratory data analysis to uncover trends, patterns, and anomalies across product verification volumes, consumer behaviour, supply chain activity, and campaign performance.
• Develop and maintain dashboards and automated reports using Power BI, Tableau, or similar tools to communicate key metrics to internal teams and clients.
• Support the Lead Data Analyst in preparing data-driven insight reports, presentations, and periodic client performance reviews.
• Write SQL queries to extract, transform, and load data from PostgreSQL and other relational databases used across Sproxil’s technology stack.
• Assist in the design and implementation of data quality frameworks, ensuring accuracy, completeness, and consistency of data across all pipelines.
• Support the analysis of CATI (Computer-Assisted Telephone Interview) survey data and field operations data for programme evaluation and reporting.
• Collaborate cross-functionally with product, engineering, service delivery, and business development teams to provide data support for decision-making.

Requirements
Qualifications & Experience

• Bachelor’s degree or its equivalent in Statistics, Epidemiology, Public Health, Biostatistics, Mathematics, Computer Science, or a related quantitative field.
• Minimum 2 years of hands-on experience in a data analyst or quantitative research role. Demonstrable exposure to public health or health programme data environments is an added advantage
• Proven practical experience in data modelling — preferable statistical, predictive, or epidemiological models — applied to real-world public health datasets (e.g. disease surveillance, HMIS data, programme evaluations).
• Proficiency in SQL for data extraction and manipulation (PostgreSQL experience preferred).
• Working knowledge of Python or R for data wrangling, analysis, and basic visualisation (Pandas, NumPy, Matplotlib, or equivalent).
• Experience with Power BI at least one other BI/visualisation tool —, Tableau, Google Looker Studio, or similar.
• Solid understanding of data cleaning, transformation, and quality assurance principles.
• Familiarity with Microsoft Excel for data analysis, including pivot tables, VLOOKUP, and basic statistical functions.

Nice to Have

• Exposure to machine learning concepts or predictive modelling.
• Experience working with supply chain, consumer engagement, or public health datasets.
• Knowledge of data pipeline tools or ETL processes.
• Familiarity with Git or version control for analytical projects.