Data Analyst

• Job Type: Officer of Administration
• Bargaining Unit:
• Regular/Temporary: Temporary
• End Date if Temporary: This is a one-year temporary hire from the date of hire.
• Hours Per Week: 35
• Standard Work Schedule:
• Building:
• Salary Range: Minimum: $67,300 -Maximum: $77,500

Position Summary

The Department of Environmental Health Sciences at Columbia University's Mailman School of Public Health seeks a full-time Data Analyst to support the research program of Dr. Darby Jack. The group studies how environmental exposures—household and ambient air pollution, extreme heat, and other climate-related hazards—affect health in low- and middle-income countries. Current work includes a long-running mother-child cohort study of household air pollution in Ghana, heat and climate-health research, and sensor-based exposure studies in Ghana and East Africa, carried out with field partners including the Kintampo Health Research Centre in Ghana and academic institutions in Ethiopia, Kenya, and Uganda.

The Data Analyst will work across this portfolio rather than on a single study. The core of the job is turning field and sensor data into analysis-ready datasets and research findings using reproducible, version-controlled workflows in R and Python. The position reports to Dr. Jack and works closely with graduate students, postdoctoral researchers, and project staff in New York, and with data teams at partner institutions abroad.

*This is a one (1) year temporary position from date of hire with the possibility for extension.

Subject to current circumstances and business needs, we may support flexible and hybrid/work arrangements. Options will be discussed during the interview process.

Responsibilities

The Data Analyst will be responsible for a variety of tasks, including, but not limited to:

• Building and maintaining data processing pipelines for high-frequency sensor data (personal and ambient air pollution monitors, temperature and humidity loggers, GPS and activity trackers), including quality control, calibration checks, and derivation of exposure metrics.
• Cleaning, documenting, and managing survey and cohort data from field studies, including linkage across data collection rounds and instruments, codebooks, and data dictionaries.
• Conducting statistical analyses in support of research questions across the portfolio, from descriptive and exploratory work through regression modeling, in collaboration with faculty and trainees.
• Developing and maintaining reproducible, version-controlled analysis workflows (Git/GitHub, R Markdown or Quarto, project-level package management) so that results can be regenerated, reviewed, and shared.
• Preparing datasets and documentation for archiving and for deposit in public and controlled-access repositories in compliance with funder data-sharing requirements.
• Producing figures, tables, and data visualizations for manuscripts, grant applications, reports, and presentations to scientific and non-specialist audiences.
• Working with field data teams at partner institutions on data collection tools, data transfer, and data quality monitoring, with occasional travel to field sites in Ghana or East Africa.
• Contributing to manuscripts and grant applications, including methods and results sections and responses to reviewer requests.

The successful candidate will have strong statistical programming skills, care about data quality and reproducibility, be comfortable working with messy real-world data from field settings, and be able to communicate results clearly to scientists, field staff, and policymakers.

Minimum Qualifications

• Requires a bachelor's degree or equivalent in education and experience, plus three years of related experience.

Preferred Qualifications

• Master's degree in biostatistics, statistics, data science, epidemiology, environmental health, computer science, or a related quantitative field; or a bachelor's degree in one of these fields with three or more years of related experience.
• Proficiency in R (tidyverse) for data manipulation, statistical analysis, and visualization, and working proficiency in Python.
• Experience with Git/GitHub and with reproducible research practices, such as literate programming (R Markdown, Quarto, or Jupyter), dependency management (renv, conda), and pipeline tools (targets, Make, or similar).
• Experience processing and managing high-frequency time-series data from sensors or other instruments, including quality control and handling of missing or malformed data.
• Experience with survey and cohort data management, including electronic data capture platforms such as REDCap, ODK, or SurveyCTO.
• Familiarity with regression methods common in epidemiology and environmental health, such as mixed-effects models and longitudinal data analysis.
• Experience preparing data and documentation for archiving and public deposit.
• Experience with research data is preferred but not required; candidates with strong data science skills from other settings are encouraged to apply.
• Ability to synthesize and communicate scientific results to a variety of partners and stakeholders, with excellent verbal and written communication skills.
• Ability to take initiative, self-manage, and work independently as well as collaboratively as part of a multidisciplinary, multi-country team.
• Exceptional attention to detail and strong organizational and time management skills.
• Interest in environmental health and climate-health research in low- and middle-income countries.

Equal Opportunity Employer / Disability / Veteran

Columbia University is committed to the hiring of qualified local residents.