BACKGROUND

The Center for Impact, Innovation and Capacity Building for Health Information Systems and Nutrition (CIIC-HIN) is a multidisciplinary research and implementation organization committed to advancing evidence-informed policy, strengthening health systems, and improving population health and nutrition outcomes. CIIC-HIN’s mandate spans health information systems and data governance, service delivery quality and patient safety, health financing and policy analysis, implementation science, climate and health, and nutrition. Working with governments, development partners, academic institutions, and civil society, the Center designs and executes solutions that enhance the generation, quality, and use of data; improve program performance and equity; and build sustainable institutional capacity aligned with Universal Health Coverage (UHC).

CIIC-HIN implements a wide portfolio of work including systems integration and optimization, routine and survey data analytics, monitoring and evaluation, operational and clinical research, capacity-building programs, and innovation pilots across public health priorities such as immunization, maternal and child health, infectious diseases, and non-communicable diseases. A core element of this portfolio is institutionalizing analytics and learning: CIIC-HIN embeds dedicated data science and M&E expertise within teams to translate data into actionable insights that inform service quality improvement, program design, resource allocation, and policy formulation at national and sub-national levels.

Objective of the POSITION

The Data Scientist will support CIICHIN to:

• Enhance institutional data analytics capacity and capabilities across projects
• Support both local and international projects implemented by CIICHIN
• Integrate and analyze indicators and datasets from multiple national and project-based platforms
• Provide evidence-based insights to improve decision-making and health outcomes
• Lead advanced statistical modeling efforts using R, Stata, or Python applying techniques such as regression, time series, Bayesian methods, geospatial analytics, and causal inference, depending on project needs.
• Embed AI and machine learning approaches into predictive modeling and health forecasting tools where applicable, to improve accuracy, relevance, and actionability.
• Actively contribute to grant proposal writing and resource mobilization efforts to strengthen sustainability

Scope of Work

The Data Scientist will be responsible for the following tasks:

• Data Integration & Management
• Analytics & Visualization
• Capacity Strengthening & Institutionalization
• Grant Development & Resource Mobilization
• Monitoring & Reporting

Qualifications and Experience

a. Educational Background

A master’s degree in data science, Statistics, Big Data Analytics, Mathematical Sciences, or a closely related field is required.

A PhD in any of the above fields is strongly preferred.

b. Professional Experience

Minimum of 3 years of applied experience in data analytics, preferably in health-related domains or public health.

Proven experience in the integration of data from multiple platforms and managing complex data pipelines.

c. Technical Competencies

Advanced skills in programming and statistical analysis using Python or R, with proficiency in SQL.

Experience with machine learning, artificial intelligence (AI) techniques, and predictive analytics—particularly for health outcome modeling—is highly desirable.

Strong command of data visualization, modeling, and interpretation for decision support.

d. Certifications

Relevant professional certifications such as DASCA’s Senior Big Data Analyst (SBDA) or Associate Big Data Analyst (ABDA) are considered an added advantage.

E. Analytical and Strategic Thinking

Demonstrated ability to translate complex datasets into policy-relevant insights and programmatic recommendations.

f. Communication and Collaboration

Excellent communication, presentation, and capacity-building skills, especially in multidisciplinary team environments.

Experience in stakeholder engagement, technical assistance, or training is a plus.

g. Additional Assets

Proven contributions to or leadership in grant proposal writing and research project design are strong advantages.

• Data Integration & Management
• Analytics & Visualization
• Capacity Strengthening & Institutionalization
• Grant Development & Resource Mobilization
• Monitoring & Reporting

• A master’s degree in data science, Statistics, Big Data Analytics, Mathematical Sciences, or a closely related field is required.
• A PhD in any of the above fields is strongly preferred.
• Minimum of 3 years of applied experience in data analytics, preferably in health-related domains or public health.
• Proven experience in the integration of data from multiple platforms and managing complex data pipelines.
• Advanced skills in programming and statistical analysis using Python or R, with proficiency in SQL.
• Experience with machine learning, artificial intelligence (AI) techniques, and predictive analytics—particularly for health outcome modeling—is highly desirable.
• Relevant professional certifications such as DASCA’s Senior Big Data Analyst (SBDA) or Associate Big Data Analyst (ABDA) are considered an added advantage.
• Demonstrated ability to translate complex datasets into policy-relevant insights and programmatic recommendations.
• Excellent communication, presentation, and capacity-building skills, especially in multidisciplinary team environments.
• Experience in stakeholder engagement, technical assistance, or training is a plus.
• Proven contributions to or leadership in grant proposal writing and research project design are strong advantages.