The University of Chicago Trust (the Trust) is a charitable trust in India, established in 2008 by alumnus of The University of Chicago. The Trust’s objectives are to provide opportunities for grants to support educational programs, research programs and activities for the betterment of India. 

The Development Innovation Lab – India (DIL-India)

DIL-India, based at the Trust in India, is dedicated to using the tools of economics to develop innovations with the potential to benefit millions of people. We have work in health, education, agriculture, climate change, road safety, and others. We do so by collaborating with government agencies, firms, and non-profits in India to identify, test, and scale evidence-based solutions. 

DIL-India has a fast-growing portfolio of policy-relevant research. DIL-India supports the development and dissemination of cutting- edge AI-based monsoon onset forecasts to farmers. DIL-India plans to build on this work, aiming to reach more people and expand its research to areas that have the potential to improve livelihoods and impact well-being.

Human Centered Weather Forecast Initiative

Collaborating with HCWF, DIL-India brings together climate scientists, artificial intelligence (AI) experts and economists to build and evaluate weather forecasts that can target what citizens need to know, leveraging novel technologies, including AI, and scientific understanding. This interdisciplinary team then works to test forecast dissemination strategies, refine those strategies based on user response, and scale forecast dissemination to inform data-driven decision-making. 

For example: The Indian Ministry of Agriculture and Farmers' Welfare distributed AI forecasts to 38 million farmers accurately predicting the start of the monsoon in 2025, becoming a model for using advanced in AI and atmospheric science to help vulnerable populations adapt to climate change. Hundreds of millions of smallholder farmers across the tropics depend on information about when the rainy season, known as the monsoon, will come each year. This advanced knowledge allows farmers to make more informed decisions about when to plant seeds, irrigate, and hire labor. However, accurate forecasting of when the monsoon will begin, especially at long lead teams and local scales, has remained a century-old challenge. AI has revolutionized global weather forecasting, promising new tools for predicting phenomena as complex as the monsoon. DIL-India researchers lead local HCWF research that supports an international, multidisciplinary collaboration to harness advances in AI to better predict the beginning of the monsoon in India.

This job opening is part of the launch of a new DIL-India initiative.

Position Description  

The Statistician, HCWF will support research activities at DIL-India in collaboration with the Human-Centered Weather Forecasts initiative. The role involves statistical design, analysis, and modeling to both support the development of blended weather models and evaluate the accuracy and impact of AI-based weather forecasting systems. This may include the design of randomized controlled trials and quasi-experimental evaluations of forecast dissemination programs across India. 

Responsibilities  

• Develop and apply statistical models to assess forecast accuracy, calibration, and skill across a range of meteorological variables and geographic settings.
• Work on the statistical development of one or more forecasts end-to-end, from model design through operational deployment and ongoing verification
• Provide input on statistical analysis plans for  and interpretation of the results of  field evaluations and A/B tests of weather forecast dissemination programs, which may include conducting power calculations or designing sampling methods for experimental and quasi-experimental studies.
• Clean, process, and analyze large datasets from weather forecast models, remote sensing, and household surveys.
• Collaborate with atmospheric scientists, data scientists, and policy teams to interpret and communicate statistical findings.
• Contribute to the preparation of research reports, policy briefs, and academic publications.
• Maintain reproducible code (e.g., in R, Python, or Stata) and support data infrastructure for research workflows.
• Performs other related work as assigned.