Research & Data Associate

Overview

Jhpiego is a nonprofit global health leader and Johns Hopkins University affiliate that is saving lives, improving health, and transforming futures. We partner with governments, health experts, and local communities to build the skills and systems that guarantee a healthier future for women and families. Jhpiego translates the best science and practices into moments of care that can mean the difference between life and death for women and families. The moment a woman gives birth, the moment a midwife helps a newborn to breathe. Through our partnerships, we are revolutionizing health care for the world’s most disadvantaged and vulnerable people. In India, Jhpiego works across various states in close collaboration with national and state governments, providing technical assistance in the areas of family planning, maternal and child health, strengthening human resources for health, and non-communicable diseases. These programs are funded by USAID, the Bill & Melinda Gates Foundation, the David & Lucile Packard Foundation, GIZ, the Children’s Investment Fund Foundation (CIFF), and other anonymous donors.

We are looking for a highly motivated and detail-oriented individual to assist in our research and learning efforts for the RISE (Reaching Impact Saturation and Epidemic Control) project. The successful candidate will work under the Senior Monitoring, Evaluation, Research, and Learning Officer, focusing on quantifying the impact of our programs on infectious disease outbreaks through rigorous mathematical/epidemiological modeling and advanced statistical techniques.

Responsibilities

• End-to-end outbreak modelling – independently code, calibrate and run basic compartmental, agent-based models or other statistical models (e.g., SIR/SIRS/SEIR, time-series regressions) to estimate outbreaks averted, life saves from specific interventions and forecast near-term trends. Use counterfactual scenarios to quantify the impact of interventions by comparing modeled outcomes with and without specific response strategies and simulate the impact of resource allocation on outbreak outcomes to inform resource planning.

• Data acquisition & cleaning – source routine surveillance and programme datasets, write reproducible scripts to clean/merge them, and document data dictionaries. Incorporate demographic factors (e.g., age, comorbidities, population density) and epidemiological data (e.g., case fatality rates, transmission rates) into models to improve the accuracy of lives saved estimates for specific populations and adjust models to account for India-specific health system constraints and regional variations in outbreak dynamics

• Parameter estimation & uncertainty analysis – fit models to data with likelihood-based or simple Bayesian methods; produce confidence/credible intervals and sensitivity checks.
• Regular analytic reports – generate clear tables, graphs, and slide decks that translate model outputs into actionable insights for programme managers and donors. Generate reports and visualizations summarizing lives saved and infections averted, tailored for policymakers, donors, and program teams
• Conduct analytics (Bayesian/MLE calibration, uncertainty quantification, ensemble forecasting) and translate results into dashboards and briefs for programme teams and donors.
• Evidence scans – conduct rapid literature reviews on modelling methods and intervention impact; keep a living repository of key parameters and priors.
• Perform systematic literature reviews and landscape analyses of outbreak-response interventions.
• Draft manuscripts, policy briefs, and donor reports; shepherd them through peer review.
• Tool development & maintenance – build and update simple R Markdown / Python notebooks or Shiny/Dash dashboards so non-technical colleagues can explore scenarios.
• Documentation & reproducibility – maintain well-commented code, version control (Git), and workflow descriptions to ensure analyses can be audited or handed over smoothly.
• Team liaison & capacity sharing – explain modelling assumptions and outputs to epidemiologists, programme staff, and leadership; train program staff on interpreting lives saved estimates and using simplified impact modeling tools for decision-making
• Collaboration with modelling groups: Engage with national modeling consortia (e.g., Indian Council of Medical Research, WHO, or academic groups) to align lives saved estimates with standardized methodologies, share best practices and contribute to collaborative modeling efforts to estimate the cumulative impact of multi-partner interventions during large-scale outbreaks.
• Emergency surge support – update models and situation summaries during outbreaks as part of the duty rota.
• Other duties as assigned by your supervisor

Required Qualifications

• Master’s degree (completed or in final semester) in Biostatistics, Epidemiology, Applied Mathematics, Data Science, Computer Science, or related field.
• Up to 2 years’ relevant experience or strong internship/thesis work involving infectious-disease modelling, statistical analysis, or data science.
• Working knowledge of R or Python for data analysis and visualisation; familiarity with version control (Git/GitHub).
• Solid grounding in statistical inference (regression, likelihood-based methods) and basic compartmental modelling concepts (e.g., SIR).
• Ability to explain quantitative findings clearly in written and verbal English; strong organisational skills and attention to detail.
• Proven teamwork mindset and willingness to learn new methods quickly.

Desirable

• Exposure to Bayesian tools (Stan, JAGS), geospatial/GIS packages, or dashboard frameworks (Shiny, Dash, Power BI).
• Coursework or project experience in infectious-disease epidemiology, vaccine impact evaluation, or health-economic modelling.
• Experience cleaning large relational datasets (SQL, PostgreSQL) or using cloud notebooks (JupyterHub, RStudio Server).
• One or more publications, preprints, or conference posters based on quantitative research.
• Familiarity with global health and Indian public healthcare delivery systems.

Jhpiego is an equal opportunity employer and offers a highly dynamic and enabling work environment.

Jhpiego offers competitive salaries and a comprehensive employee benefits package. Women candidates are encouraged to apply.

Due to the high volume of applications, only shortlisted applicants will receive a response from Jhpiego

HR

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