Atmospheric Scientist, Human-Centered Weather Forecasts
About the Trust
The University of Chicago Trust (the Trust) is a charitable trust in India, established in 2008 by an alumnus of The University of Chicago. The objectives of the UChicago Trust are to provide opportunities for grants to support agriculture, water, educational programs, research programs, and activities for the betterment of India.
About The Development Innovation Lab - India (DIL-India)
DIL-India, based at the Trust in India, is dedicated to using the tools of economics to benefit millions of people. We work across sectors like 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.
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 Atmospheric Scientist Lead, HCWF will support the creation of advanced, built-for-purpose forecasts of high-impact weather phenomena for deployment at scale in low- and middle-income countries. As part of a multidisciplinary collaboration between universities, development agencies, and governmental ministries, the selected candidate will contribute to the development of forecasts of dangerous heat stress, precipitation, monsoon onset, and other tropical phenomena using AI, statistical, and physics-based weather prediction models. This position is focused on large-scale delivery of decision-oriented forecasts together with research and development. The position is well-suited for candidates with expertise in one or more of the following areas: monsoon onset prediction; short- or medium-range precipitation forecasting; tropical intraseasonal variability; and extreme heat and heat stress forecasting.
Responsibilities
Contribute to the development of novel decision-oriented forecasting metrics and forecasting systems using AI, statistical, and physics-based methods; evaluate forecast skill against baselines tailored for decision-makers.
Build fundamental understanding of the forecasting potential for high-impact phenomena, such as extreme heat and monsoon onset, using AI and statistical weather models.
Collaborate with meteorological agencies in multiple countries to help build an operational system providing actionable, real-time predictions, leveraging last-mile delivery pipelines developed by non-governmental partner organizations.
Conduct benchmarking and validation of AI-based and physics-based forecast systems against observational data.
Collaborate with data scientists and ML engineers to build scalable and reproducible forecast pipelines.
Contribute to the design of field experiments, including randomized controlled trials, to evaluate the impact and uptake of improved forecasts.
Contribute to grant proposals and the preparation of research reports, technical documents, and academic publications.
Performs other related work as assigned.
Qualifications
Education
Masters in atmospheric science, meteorology, climate science, agrometeorology, physical oceanography, applied math, statistics, physics, engineering, or a related field.
Advanced degree preferred.
Experience
Minimum of 3 or more years of work experience in a related job discipline, including research or operational roles in weather forecasting or climate science.
Experience applying machine learning or statistical methods to weather or climate prediction.
Experience with numerical weather prediction (NWP) systems.
Experience working in low- and middle-income country contexts, and data-sparse environments.
Experience with field work in developing countries, preferably including randomized controlled trials is preferred
Competencies
Strong programming and numerical skills; proficiency in Python and common scientific computing libraries (e.g., NumPy, xarray, PyTorch, TensorFlow).
Experience with high-performance computing and analysis of large meteorological or climate datasets (e.g., reanalyses and NWP or AI weather prediction ensemble products).
Expertise in weather forecasting, climate modeling/dynamics, or related geospatial applications.
Familiarity with theoretical and practical aspects of scientific deep learning and AI weather models (e.g., GraphCast, AIFS).
Knowledge of forecast verification methods (e.g., CRPS, reliability diagrams, skill scores).
Strong communication and presentation skills, including ability to present to non-specialist audiences.
Ability to work both independently and as part of a fast-paced, multidisciplinary, multi-institutional team.
Working Conditions
Based in Delhi with regular travel throughout India to project locations
Applicant must be legally authorized to work in India.
This is a one-year position subject to renewal.
Application Documents
Resume
Cover Letter
Three References