Research Data Analyst

SCHOOL/UNIT DESCRIPTION:

The Political Accountability and Climate Transition (PACT) lab is funded by the Stanford King Center on Global Development under a new initiative on Climate Change and Governance in Low- and Middle-Income Countries that was launched in 2024 to address the research gap by studying a range of topics related to climate politics in the global south. 

The initiative’s first project was to develop a novel, algorithmic tool that allows it to compare survey information from across countries and years. Using the tool, the team has created a comprehensive database of Afrobarometer survey questions combined with geocoded measures of environmental exposure to give a picture of the association between climate change and public opinion over time. Another project involves hyper-localized informational treatments on climate’s long-term costs in an effort to make the scale of the crisis understandable to ordinary citizens. The initiative also has plans to conduct broad public opinion surveys in emerging economies to get a better sense of how citizens perceive and understand climate change and also how they want their governments to respond to the problem.

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• We create a hub of innovation through the power of diversity of disciplines and people.
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POSITION SUMMARY:

The Political Accountability and Climate Transition (PACT) lab is seeking a Research Data Analyst 1 (1 year fixed term) to assist in the creation of a high-performance spatial data processing pipeline focused on the use of high-resolution climate products and other spatial data in downstream inference tasks. The RDA will be responsible for working in consultation with the project PIs (Amanda Kennard is a principal investigator) to build the pipeline and manage the resulting database. The project’s primary goal is the creation of a first-of-its-kind database of climate exposures and other spatial covariates that is explicitly designed for use in climate impacts research, primarily but not exclusively in environmental economics and political economy. Other tasks include the acquisition and cleaning of new climate products and other spatial microdata including high-resolution estimates of poverty and conflict; descriptive analyses and validation exercises; and preliminary analyses using typical workhorse estimators in the field of economics and political economy. As such, we are seeking a candidate with expert-level coding ability in R and intermediate ability in Python as well as ample experience working with spatial data and associated libraries. Experience with cloud computing and some database management is also highly desirable. We will also be implementing cutting-edge approaches to correcting remotely sensed measurements for use in downstream inference tasks, so a strong background in frequentist statistics will be an advantage. 

The primary responsibility of the RDA will be incorporating functions from existing packages into a highly streamlined and generalizable workflow for the processing of spatial data of all kinds and, where necessary, writing new functions. The RDA should be comfortable writing well-annotated, functionalized code that makes heavy use of parallelization and new zero-copy shell data formats such as Zarr, Parquet, and GeoParquet. A coding test will be given to all candidates who make the interview round. The coding test will include timed, short-duration exercises in which GPT and other LLMs may be used as well as longer-form exercises that test the ability to create functionalized workflows and validate their output. The ideal candidate will be comfortable working with very large datasets, creating and managing Github repositories, producing clear and concise documentation, and communicating with PIs and other stakeholders to add new functionality to the existing pipeline. This position is ideal for a data scientist or statistician hoping to enter graduate school in social or computational science.

CORE DUTIES:

• Collect, manage and clean datasets.
• Employ new and existing tools to interpret, analyze, and visualize multivariate relationships in data.
• Create databases and reports, develop algorithms and statistical models, and perform statistical analyses appropriate to data and reporting requirements.
• Use system reports and analyses to identify potentially problematic data, make corrections, and determine root cause for data problems from input errors or inadequate field edits, and suggest possible solutions.
• Develop reports, charts, graphs and tables for use by investigators and for publication and presentation.
• Analyze data processes in documentation.
• Collaborate with faculty and research staff on data collection and analysis methods.
• Provide documentation based on audit and reporting criteria to investigators and research staff.
• Communicate with government officials, grant agencies and industry representatives.