1. Background
PharmAccess Foundation works to improve access to quality healthcare in Africa by applying data, digital solutions, and innovative financing in practical, high-impact ways. Data plays a central role across our programmes, products, and innovation initiatives.
Working with data at PharmAccess means operating in real-world complexity: incomplete datasets, evolving questions, and diverse stakeholders. From a data perspective we work in a difficult environment with the opportunity to make big impact. We value curiosity, pragmatism, and experimentation over perfect or purely academic solutions.
We are looking for a Data & AI Analyst who enjoys exploring data, experimenting with AI and predictive modelling, and working closely with colleagues across the organisation.
2. Purpose of the role
This role focuses on analysing, unlocking, and applying data to support decision-making, and innovation. It sits at the intersection of data analysis and applied data science, with strong exposure to AI and predictive modelling.
The role is ideal for someone with a broad data science profile, equally comfortable taking on data engineering tasks as developing predictive models, who wants to stay close to real organisational needs and see their work lead to decisions on the ground. Every project will be different, your goal will be to create what is needed for the project.
3. Key responsibilities
Data analysis & insights
- Analyse and interpret data from internal and external sources.
- Unlock, structure and combine datasets for analyses, dashboards, and models.
- Actively refine stakeholder questions into well-defined analytical questions and hypotheses that guide the design of data-driven insights.
Predictive analytics & AI
- Develop and apply predictive models (e.g. for epidemic preparedness).
- Experiment with AI and machine learning techniques in applied settings.
Products, innovation & automation
- Contribute to the development of Atlas, our AI chatbot for the PharmAccess data warehouse.
- Identify opportunities to automate, improve, or rethink how data is used.
- Contribute to a culture of experimentation and continuous learning.
4. Collaboration
- Work closely with data engineers, data scientists, and other members of the international Data Chapter.
- Collaborate with programme teams and other internal stakeholders.
- Engage with external partners where relevant.
- Within these collaborations, ownership and decision making is expected from you on the data part
5. Profile
We care more about how you work and think than about ticking every box.
- Strong conceptual and critical thinking skills, proven ability to independently translate broad questions into well-defined analytical questions and hypotheses.
- Bachelor’s or Master’s degree in a relevant field (e.g. Data Science, Statistics, Computer Science, AI).
- Around 2–5 years of experience in data science or a similar role.
- Comfortable working with Python and SQL (knowledge about Snowflake & DBT is a plus)
- Hands-on familiarity with AI and machine learning concepts, and strong motivation to develop further.
- Curious, proactive, and comfortable working with ambiguity.
- Able to communicate clearly with both technical and non-technical colleagues.
You don’t need to tick every box, we value curiosity and learning over perfection.
6. What success looks like
- The role contributes to continuous improvement in how data is collected, processed, and used by identifying inefficiencies and proposing practical enhancements.
- Useful insights that support better decisions.
- Tangible contributions to AI- and data-driven innovation projects.
- Clear progress in the development and adoption of Atlas and other data products.
- Increasingly effective use of data and automation across the organisation through well-designed, efficient data processes.