About Maisha Meds

Maisha Meds is an organization dedicated to improving health care in Africa through best-in-class technology. Founded in 2017, Maisha Meds has created the largest digital network of private pharmacies and clinics across Kenya, Tanzania, Uganda, Nigeria, and Zambia through our mobile software. Our platform not only helps these providers improve their business by making sales, managing inventory, and tracking patients. It also reimburses them for providing high-quality care for malaria, family planning, and HIV prevention at discounted costs.

Maisha Meds logs over 24 million patient visits every year and has provided over one million patient reimbursements to date. We harness data from our network of pharmacies and clinics to reveal health and market trends, which allows us to design better solutions that work for the people we serve. We have worked with leading academic institutions such as UC Berkeley, Emory University, and KEMRI to evaluate the effectiveness of our programs. Research shows that our system is able to significantly increase the uptake of long-acting contraceptives and appropriate malaria case management.

Our work is funded by a range of partners, including scale-up funding from USAID Development Innovation Ventures and the Bill & Melinda Gates Foundation. This will help Maisha Meds greatly expand its mobile software to 7,500 total pharmacies and clinics by late 2026, delivering subsidized care to several million new patients in the process.

About the Role

Maisha Meds is seeking a mid-career data scientist with machine learning and statistics experience to join our data team. This role will focus on automating and scaling data cleaning and validation workflows, implementing machine learning features within an Android application, and contributing to the development of new data products.

You’ll work on deploying real-world ML solutions in a complex environment,and collaborating closely with the product and engineering teams. Example projects may include building models for sales and stock-out forecasting, developing intelligent in-app features, and improving how we process and analyze large-scale health and retail datasets.

This is a hands-on, highly collaborative role in a flexible, mission-driven environment—ideal for someone who enjoys applying machine learning to practical problems and enhancing real-world systems through data science.

• Design and implement end-to-end machine learning models using Python and relevant libraries, prioritizing production readiness and scalability
• Develop forecasting, time-series, and anomaly detection models
• Deploy models in resource-constrained environments like Android devices or lightweight back-end systems
• Build and maintain machine learning workflows, including data cleaning, feature engineering, and validation pipelines
• Evaluate model performance of off-the-shelf LLM and OCR tools, and provide recommendations for improvements
• Handle large, messy, or incomplete datasets from multiple sources to generate reliable insights
• Use data tools such as AWS, Terraform, dbt, Rivery, and Looker to support data infrastructure and workflows
• Proficiency in statistical methods and evaluating machine learning models