About Clinton Health Access Initiative- Rwanda (CHAI)

The Clinton Health Access Initiative, Inc. (CHAI) is a global health organization committed to saving lives and reducing the burden of disease in low-and middle-income countries.

About This Opportunity

The position of Associate, Data Scientist at Clinton Health Access Initiative- Rwanda (CHAI) located in Kigali, Rwanda offers an opportunity for qualified professionals to contribute their knowledge and experience in a dynamic working environment. The organization seeks individuals who are motivated, reliable, and capable of performing their duties effectively while maintaining professional standards.

CHAI RWANDA

VACANCY ANNOUNCEMENT

Title: Associate, Data Scientist

Program: Health Systems Strengthening

Job Location: Kigali, Rwanda

Type: Full-Time Paid

Start date: Immediate

Overview

The Clinton Health Access Initiative, Inc. (CHAI) is a global health organization committed to our mission of saving lives and reducing the burden of disease in low-and middle-income countries. We work at the invitation of governments to assist them and the private sector to create and sustain high-quality health systems.

CHAI was founded in 2002 in response to the HIV/AIDS epidemic, with the goal of dramatically reducing the price of life-saving drugs and increasing access to these medicines in the countries with the highest burden of the disease. Over the following two decades, CHAI has expanded its focus. Today, along with HIV, we work in conjunction with our partners to prevent and treat infectious diseases such as COVID-19, malaria, tuberculosis, and hepatitis. Our work has also expanded into cancer, diabetes, hypertension, and other non-communicable diseases, and we work to accelerate the rollout of lifesaving vaccines, reduce maternal and child mortality, combat chronic malnutrition, and increase access to assistive technology. We are investing in horizontal approaches to strengthen health systems through programs in human resources for health, digital health, and health financing. With each new and innovative program, our strategy is grounded in maximizing sustainable impact at scale, ensuring that governments guide the solutions, that programs are designed to scale nationally, and learnings are shared globally.

At CHAI, our people are our greatest asset, and none of this work would be possible without their talent, time, dedication, and passion for our mission and values. We are a highly diverse team of enthusiastic individuals across 40 countries with a broad range of skill sets and life experiences. CHAI is deeply grounded in the countries we work in, with the majority of our staff based in program countries. Learn more about our exciting work: http://www.clintonhealthaccess.org

CHAI is an Equal Opportunity Employer, and is committed to providing an environment of fairness, and mutual respect where all applicants have access to equal employment opportunities. CHAI values diversity and inclusion and recognizes that our mission is best advanced by the leadership and contributions of people with diverse experience, backgrounds, and culture.

Programs Overview

The Government of Rwanda (GoR) strives to achieve Universal Health Coverage (UHC) by enabling equitable access to quality health services for the population as outlined in the Fourth Health Sector Strategic Plan (HSSP IV). This commitment has been made against a backdrop of rising health care costs and uncertainty around the future availability of donor funding and calls for improved health sector sustainability. Acknowledging this, the Government aims to employ effective, equitable, efficient, and sustainable health care financing and systems strengthening, putting an emphasis on primary health care as the cornerstone of a strong and sustainable health system.

CHAI Rwanda’s Health System Strengthening (HSS) programs work on a day-to-day basis with Government leadership in the Ministry of Health (MoH) and the Rwanda Social Security Board (RSSB). The goal of those programs is to assist the Government to accelerate progress towards UHC by ensuring the sustainable delivery of quality, essential health services. This includes designing and implementing national Primary Health Care (PHC) reforms from health center level down to community health, to increase health workforce availability, to prioritize and sustainably finance essential services, and to assist improved management, financing, and delivery of quality primary health care.

Position overview

CHAI currently seeks an Associate Data Scientist to collaborate with the Ministry of Health (MOH), reporting directly to the head of data science at the National Health Intelligence Center to help enhance the quality, governance, and use of evidence on policy decision making. S/He also reports in parallel to CHAI, HSS digital health program manager for specific CHAI supported initiatives.

The Associate Data Scientist is tasked with leading advanced data analytics, statistical modeling, and applied machine learning across priority public health and clinical domains. The role ensures that health data is transformed into high-quality, reliable, explainable, and decision-ready intelligence that informs AI development, policy decisions, and health system performance improvement. This role serves as the technical bridge between raw health data, AI models, and real-world decision-making, ensuring scientific rigor, reproducibility, and national-scale usability.

This is a top government priority for MOH, and the Associate, Data Scientist will therefore be seconded to the MOH/NHIC, S/He also reports in parallel to CHAI, HSS digital health program manager for specific CHAI-supported initiatives.

Job responsibilities:

The key functions and deliverables of this role will include:

Advanced Data Analytics and Statistical Modeling

guide advanced descriptive, predictive, and inferential analyses across clinical, surveillance, and health systems datasets.

create and validate predictive models, risk stratification algorithms, forecasting models, epidemiological and operational analytics

Apply rigorous statistical methods, hypothesis testing, and causal inference techniques to assist decision-making.

Ensure all analytics are clinically interpretable, policy-relevant, and scientifically defensible.

Feature Engineering & Machine Learning assist

Design robust feature engineering pipelines from EMRs, HMIS, LMIS and surveillance platforms

assist AI Engineers with high-quality training datasets, feature selection, bias detection and data representativeness testing

Contribute to development of machine learning models, particularly in tabular health data, time-series forecasting and population-level risk prediction.

Data Quality, Governance and Integrity

guide implementation of data quality frameworks, completeness, consistency, accuracy and timeliness checks

Perform data audits, outlier detection, bias and missing data analysis

Ensure compliance with national health data governance standards, privacy and ethical data use policies.

keep full data documentation, metadata standards, and lineage tracking.

Health Systems Intelligence & Decision assist

create decision-assist analytics and public health dashboards for disease burden tracking,

resource allocation and service delivery optimization.

Translate analytical outputs into policy briefs, executive dashboards, technical report.

Ensure outputs are actionable, explainable, and aligned with the national health priorities.

Data Engineering assist and Pipeline optimization

Work closely with Data engineers, integration teams and AI engineers to ensure analytics assets are scalable, reproducible and production ready.

assist data pipeline optimization, ETL quality validation and real-time and batch processing readiness

Mentorship, Capacity Building & Collaboration

Mentor junior data scientists and analysts

guide internal analytics communities of practice, code reviews and methodology workshops

Serve as a technical reviewer for analytics protocols, research designs and data science deliverables

Innovation and Continuous Improvement

Stay current with advances in Machine Learning, Statistical Science, Health Analytics, Artificial Intelligence and Causal Inference trends

Propose innovative analytics and data science use cases for public health, service delivery, health financing, supply chain and logistics for pharmaceuticals and disease surveillance

Required Qualifications

MSc degree in relevant discipline (Data Science, Applied Mathematics, Actuarial Science, Operations Research, Statistics, Epidemiology, Health Informatics)

5-8 years of professional experience in the field of advanced analytics and/or applied statistics preferably with Data Science applications

Solid mathematical foundation and knowledge of a variety of statistical methods (e.g. regression, classification, clustering, experimental design, modeling, advanced forecasting)

Proven experience working with large-scale datasets, regulated data environments, preferably within a health domain.

Strong proficiency in data visualization, demonstrated experience in Python, R, SQL, and working with structured, semi-structured and unstructured datasets

Ability to focus on vaguely defined problems requiring the application of a creative approach.

Strong teamworking skills

Excellent written and verbal communication skills for coordinating across teams.

Ability to mentor and guide teams in a fast-paced and changing environment.

• Guide advanced descriptive, predictive, and inferential analyses across clinical, surveillance, and health systems datasets.
• Create and validate predictive models, risk stratification algorithms, forecasting models, epidemiological and operational analytics.
• Apply rigorous statistical methods, hypothesis testing, and causal inference techniques to assist decision-making.
• Ensure all analytics are clinically interpretable, policy-relevant, and scientifically defensible.
• Design robust feature engineering pipelines from EMRs, HMIS, LMIS and surveillance platforms.
• Assist AI Engineers with high-quality training datasets, feature selection, bias detection and data representativeness testing.
• Contribute to development of machine learning models, particularly in tabular health data, time-series forecasting and population-level risk prediction.
• Guide implementation of data quality frameworks, completeness, consistency, accuracy and timeliness checks.
• Perform data audits, outlier detection, bias and missing data analysis.
• Ensure compliance with national health data governance standards, privacy and ethical data use policies.
• Keep full data documentation, metadata standards, and lineage tracking.
• Create decision-assist analytics and public health dashboards for disease burden tracking.
• Resource allocation and service delivery optimization.
• Translate analytical outputs into policy briefs, executive dashboards, technical reports.
• Ensure outputs are actionable, explainable, and aligned with the national health priorities.
• Work closely with Data engineers, integration teams and AI engineers to ensure analytics assets are scalable, reproducible and production ready.
• Assist data pipeline optimization, ETL quality validation and real-time and batch processing readiness.
• Mentor junior data scientists and analysts.
• Guide internal analytics communities of practice, code reviews and methodology workshops.
• Serve as a technical reviewer for analytics protocols, research designs and data science deliverables.
• Stay current with advances in Machine Learning, Statistical Science, Health Analytics, Artificial Intelligence and Causal Inference trends.
• Propose innovative analytics and data science use cases for public health, service delivery, health financing, supply chain and logistics for pharmaceuticals and disease surveillance.

• Communication
• Teamwork
• Leadership
• Management
• Data analysis
• Excel
• Audit
• Research
• Training
• Logistics
• Human resources
• Python
• R
• SQL
• Data visualization
• Statistical modeling
• Machine learning

• MSc degree in relevant discipline (Data Science, Applied Mathematics, Actuarial Science, Operations Research, Statistics, Epidemiology, Health Informatics)
• 5-8 years of professional experience in the field of advanced analytics and/or applied statistics preferably with Data Science applications
• Solid mathematical foundation and knowledge of a variety of statistical methods (e.g. regression, classification, clustering, experimental design, modeling, advanced forecasting)
• Proven experience working with large-scale datasets, regulated data environments, preferably within a health domain.
• Strong proficiency in data visualization, demonstrated experience in Python, R, SQL, and working with structured, semi-structured and unstructured datasets
• Ability to focus on vaguely defined problems requiring the application of a creative approach.
• Strong teamworking skills
• Excellent written and verbal communication skills for coordinating across teams.
• Ability to mentor and guide teams in a fast-paced and changing environment.