This position has been deemed critical by the Stanford Doerr School of Sustainability Dean’s Office and is exempt from the hiring pause.
This position is fixed-term for two years with the possibility of extension based on funding availability and programmatic need.
This position is based on Stanford’s main campus with consideration given to the option for a hybrid work schedule (partially onsite and offsite), subject to operational need. Interested candidates must include a resume and cover letter to be considered for this position.
Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa.
About Us
The Stanford Doerr School of Sustainability strives to create a future when humans and nature thrive in concert and in perpetuity. The school is made up of a three-part structure to drive global impact: Our academic departments and programs educate students and create new knowledge across areas of research that are crucial for advancing the long-term prosperity of the planet and people; Institutes bridge scholarship at Stanford and beyond, bringing multiple viewpoints to bear on urgent challenges; The Sustainability Accelerator drives new policy and technology solutions through a worldwide network of partners who work with our teams to develop solutions at a global scale. The school is dedicated to creating and supporting a community with the richness of experience and background needed to create solutions that benefit all people, particularly those most affected by environmental damage and climate change.
For more information on the school, click here .
The Stanford Woods Institute for the Environment works to advance global sustainability through new knowledge and practical, fair solutions to pressing environmental challenges. As part of the Stanford Doerr School of Sustainability, the Woods Institute provides an interdisciplinary research hub for scholars from all seven schools at Stanford. Offering targeted research incentives as well as convening, education, and leadership programs, the Woods Institute supports faculty, researchers, and students as they investigate and advance solutions to critical climate, environment, and sustainability challenges. Working on campus and around the globe, the Woods Institute and its hosted centers and programs combine cutting edge scholarship, diverse partnerships, and deep engagement across sectors and disciplines to advance just, practical solutions for people and the planet.
The Center for Human and Planetary Health , launched in October 2024, is the newest Center hosted at the Woods Institute, in close collaboration with the Center for Innovation in Global Health at the Stanford School of Medicine. The Center advances research, solutions and education to improve health and sustainability. The Center’s lead contamination research initiative, now called Project Unleaded , is a critical part of the new Center. Project Unleaded’s efforts began in 2020 and have spanned 6 countries with 7 external partners. Project Unleaded prioritizes policy-relevant research and deep engagement with local partners in affected countries to achieve lasting change. Project Unleaded’s mission is to create a healthier world by addressing lead poisoning at the source. Research priorities center around 3 goals:
- To identify critical lead exposure sources linked with the human lead burden
- To develop, test and scale-up interventions to eliminate sources of lead
To advance lead detection techniques
Project Unleaded is looking for a Research Analyst to lead quantitative research projects that work to identify and mitigate lead contamination in spices and other environmental sources.
The Research Analyst will work with complex, multi-source datasets and apply advanced statistical methods - including complex survey designs, advanced regression techniques, spatial analysis, and probabilistic modeling - to address complex research questions. The Research Analyst will also help manage datasets and contribute to the design of data collection tools (e.g., REDCap, SurveyCTO, Qualtrics), support real-time data quality control, and contribute to translating rigorous analyses into actionable insights for policy, intervention design, and scientific publications - in collaboration with senior researchers. The ideal candidate is a highly motivated and technically rigorous analyst with strong foundations in biostatistics, epidemiology, data science, econometrics, health policy, global health, or a related discipline. They have proficiency in R (preferred) or similar statistical programming languages (e.g., Python, Stata, SAS), and familiarity with complex survey and multi-level or cluster-correlated datasets. They are eager to troubleshoot complex programming challenges, apply relevant methodological literature, and design scalable, well-documented analytical workflows that incorporate best practices. Experience with GIS tools, simulation, and integrating high-performance computing or AI/LLM tools into analysis pipelines is highly valued. Beyond technical skills, this person is organized, proactive, and able to meet deadlines, while contributing to a collaborative, mission-driven research environment. This position may require some international travel as well as flexible working hours to accommodate partner time zones, is based on Stanford’s campus, and reports to the Director of Project Unleaded.
Your Responsibilities will include:
- Coordinate research protocols and scientific activities with international partners in low-income country settings
- Manage study administration including data use agreements, study and institutional review board protocols
- Conduct data cleaning, processing and statistical analyses using epidemiologic and environmental datasets, primarily using R statistical software
- Design, document, and execute reproducible data-analysis pipelines that apply relevant statistical methods to effectively answer relevant research questions, while acknowledging potential sources of bias, confounders, and limitations
- Draft tables, figures and written summaries of findings to elucidate actionable insights for scientific manuscripts, NGO and government stakeholders
- Create and maintain databases/dashboards for quality control tracking during data collection and coordinate corrections with collaborators as needed
- Ensure data quality and analyze calibration data of quantitative measurements in real-time
- Synthesize information and/or conduct literature reviews to support manuscript preparation and scientific presentations or proposals - in the form of both writing and data visualization
Train other research assistants on the work described above. Create documentation in a lab manual for current and future trainees.
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In addition to the minimum qualifications listed below, our preferred qualifications include:
- Statistical Theory: Strong understanding of statistical concepts including but not limited to: complex survey designs, data collection tools (RedCap, SurveyCTO, Qualtrics), advanced regression techniques, and spatial analysis.
- Programming Languages: Advanced proficiency in R (preferred) or other analogous statistical programming languages (e.g. Python, Stata, SAS). Fluency across multiple programming languages is a bonus.
- Organizational skills: Experience with automation, data engineering, data cleaning, scripting, and the design of scalable technical solutions for stakeholder communications and implementing best data hygiene practices.
- Domain experience: Familiarity with environmental health and/or epidemiology to inform the analysis of lead exposure research. Experience analyzing global health datasets (e.g. Demographic Health Surveys, Multiple Indicator Cluster Surveys, National Lead Surveys)
- Experience collaborating with global partners and on global health research topics
- Clustered Data Analysis: Experience analyzing complex survey designs or using specialized techniques for analyzing multi-level and cluster-correlated datasets
- Probabilistic Modelling: Experience with simulation, modelling, and sensitivity analyses to address uncertainty
- Geospatial Integration: Experience working with geospatial datasets and geographic information systems (GIS) libraries for spatial analysis.
AI & HPC Integration: Experience leveraging LLMs and computer clusters into data analysis pipelines to efficiently produce clean, understandable workflows.
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The expected pay range for this position is $85,000 to $104,477 per annum.
