Institute Overview

Ifakara Health Institute (IHI) is a leading research organization in Africa with a strong track record in developing, testing and validating innovations for health. Driven by a core strategic mandate for research, training and services, the Institute’s work now spans a wide spectrum, covering biomedical and ecological sciences, intervention studies, health-systems research, service delivery and policy translation.

Position Summary

Ifakara Health Institute under Maternal and Newborn Health Program is seeking a Data Scientist to be based at Dar es Salaam station, reporting to Data Manager. The ideal candidate will analyse and translate maternal and newborn health data into actionable insights to inform program implementation, performance monitoring, quality improvement, and evidence-based decision-making. The position will use advanced statistical methods, machine learning, and artificial intelligence to identify trends, assess program performance, and support predictive analysis of maternal and newborn health outcomes. The data scientist will work closely with the MEL team to develop interactive dashboards, conduct data quality assessments, support health facility assessments, and generate analytical reports for program management and stakeholders. Working closely with the MEL team, statisticians, health workers, and MoH and government counterparts, the position will also strengthen data literacy, analytical capacity, and routine data use at national, regional, district, and facility levels. The ideal candidate must be ready and available to assume duties immediately upon appointment.

Responsibility

• Data Analysis Analyse maternal and neonatal data as tasked by the program, generating actionable insights and programmatic recommendations.
• Machine Learning & AI Apply machine learning and AI tools to analyse health data, identify trends, and support predictive modelling for maternal and neonatal health outcomes.
• Dashboard Development Design and maintain interactive data visualisations using R Shiny or Python to support program monitoring and reporting.
• Training & Capacity Building Collaborate with statisticians to conduct data literacy training for healthcare workers at various levels (MoH, Regional, District, PORALG).
• Data Visualisation Create compelling visual reports and presentations to communicate findings to stakeholders and non-technical audiences.
• Data Quality Assessments Conduct regular Data Quality Assessments (DQA) to identify trends and address systematic data collection issues.
• Supervision Offer support and supervision in Health Facility Assessments (HFA) to ensure data consistency and adherence to research protocols.
• Methodology Review Regularly review and update data analysis protocols to incorporate new statistical methods, machine learning approaches, and program requirements.
• Reporting Prepare regular analytical reports and dashboards to inform program decision-making and donor reporting.

Skill

• Statistical Programming Advanced skills in Python, R, Shiny, and STATA for statistical reporting and data visualisation.
• Machine Learning & AI Knowledge and practical experience with machine learning algorithms and AI tools for health data analysis and predictive modelling.
• Programming Working knowledge of Python for data analysis and scripting.
• Data Manipulation Expertise in handling large datasets, including data cleaning, recoding, and validation.
• Data Management Working knowledge of SQL and MySQL for querying relational databases.
• Training Strong ability to train and manage teams effectively, with experience building healthcare workers data literacy capacity.
• Communication Excellent interpersonal skills to collaborate with statisticians, healthcare professionals, and governmental organisations.
• Statistical Programming Proficiency Strong proficiency in R or Python for data wrangling, statistical analysis, visualisation, and reproducible reporting.
• Real-World Data Analysis Demonstrated ability to analyse messy, multi-source, routinely collected data and generate credible, actionable insights under real-world constraints.
• Statistical Reasoning Solid grounding in statistical reasoning, including bias, confounding, missing data, and uncertainty.
• Advanced Statistical Analysis Advanced proficiency in statistical analysis and familiarity with other statistical software (e.g., Stata, SAS, HLM).
• Analytical & Visualisation Tools Familiarity with advanced analytical or visualisation tools (e.g., Shiny, ggplot2, plotly, pandas, matplotlib, NumPy, SciPy).
• Data Ethics & Security Awareness of data security, responsible data use, and ethical considerations in low-resource health system contexts.

Qualification Required

A candidate must have a master degree in Data Sccience, Biostatistics, Computer Science, Statistics, Health Informatics, or related fields.

Experience

• Experience Minimum of 2–5 years of experience in data analysis or data science roles, especially within health-related projects.
• Technical Experience Proven experience working with health datasets, including data extraction, cleaning, analysis and visualization.

No. of Position

1

Preferred Language Skill

Good written and spoken English and Kiswahili.

Remuneration

An attractive and competitive remuneration package shall be offered to the successful candidates as per IHI salary scales.

Equal Employment Opportunity

Ifakara Health Institute is an equal employment opportunity employer. We are committed to providing a workplace free from discrimination and harassment. We prohibit discrimination of any kind during recruitment and employment. All employment decisions are based solely on job requirements, merit, and individual qualifications, in compliance with the labor laws of Tanzania.

• Data Analysis Analyse maternal and neonatal data as tasked by the program, generating actionable insights and programmatic recommendations.
• Machine Learning & AI Apply machine learning and AI tools to analyse health data, identify trends, and support predictive modelling for maternal and neonatal health outcomes.
• Dashboard Development Design and maintain interactive data visualisations using R Shiny or Python to support program monitoring and reporting.
• Training & Capacity Building Collaborate with statisticians to conduct data literacy training for healthcare workers at various levels (MoH, Regional, District, PORALG).
• Data Visualisation Create compelling visual reports and presentations to communicate findings to stakeholders and non-technical audiences.
• Data Quality Assessments Conduct regular Data Quality Assessments (DQA) to identify trends and address systematic data collection issues.
• Supervision Offer support and supervision in Health Facility Assessments (HFA) to ensure data consistency and adherence to research protocols.
• Methodology Review Regularly review and update data analysis protocols to incorporate new statistical methods, machine learning approaches, and program requirements.
• Reporting Prepare regular analytical reports and dashboards to inform program decision-making and donor reporting.

• Statistical Programming Advanced skills in Python, R, Shiny, and STATA for statistical reporting and data visualisation.
• Machine Learning & AI Knowledge and practical experience with machine learning algorithms and AI tools for health data analysis and predictive modelling.
• Programming Working knowledge of Python for data analysis and scripting.
• Data Manipulation Expertise in handling large datasets, including data cleaning, recoding, and validation.
• Data Management Working knowledge of SQL and MySQL for querying relational databases.
• Training Strong ability to train and manage teams effectively, with experience building healthcare workers data literacy capacity.
• Communication Excellent interpersonal skills to collaborate with statisticians, healthcare professionals, and governmental organisations.
• Statistical Programming Proficiency Strong proficiency in R or Python for data wrangling, statistical analysis, visualisation, and reproducible reporting.
• Real-World Data Analysis Demonstrated ability to analyse messy, multi-source, routinely collected data and generate credible, actionable insights under real-world constraints.
• Statistical Reasoning Solid grounding in statistical reasoning, including bias, confounding, missing data, and uncertainty.
• Advanced Statistical Analysis Advanced proficiency in statistical analysis and familiarity with other statistical software (e.g., Stata, SAS, HLM).
• Analytical & Visualisation Tools Familiarity with advanced analytical or visualisation tools (e.g., Shiny, ggplot2, plotly, pandas, matplotlib, NumPy, SciPy).
• Data Ethics & Security Awareness of data security, responsible data use, and ethical considerations in low-resource health system contexts.

• A candidate must have a master degree in Data Sccience, Biostatistics, Computer Science, Statistics, Health Informatics, or related fields.