Global Health Data Analyst - Python Pipelines & Insights

University of Washington

Seattle (WA)

On-site

USD 78,000 - 90,000

Full time

14 days+

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Job summary

The Institute for Health Metrics and Evaluation (IHME) at the University of Washington is seeking a Temporary Data Analyst to turn complex health data into actionable insights and support publications across multidisciplinary research projects.

Responsibilities include collaborating with researchers, preparing data pipelines in Python, and maintaining reproducible analyses. Requires a bachelor's degree with related experience and strong English communication.

Qualifications

Bachelor’s degree in social sciences, engineering, computer science, or related field plus two years’ related experience.
Proficiency with Python (pandas, NumPy) for data analysis tasks including cleaning, transformation, visualization, and statistical evaluation.
Ability to evaluate and integrate emerging technologies into data workflows to improve scalability, accuracy, and reproducibility of analyses.
Interest in global health, population health, and how quantitative research and data science create valuable global public goods.
Experience working with large secondary datasets.
Strong written and spoken English communication.
Demonstrated self-motivation, attention to detail, flexibility, and ability to thrive in a fast-paced, creative, entrepreneurial environment.
Ability to learn new information quickly and apply analytic skills to understand complex information systematically.
Strong quantitative aptitude
Flexible attitude and willingness to move between different research teams to gain broader experience.

Responsibilities

Familiarize with substantive areas of expertise to understand health data dimensions and the analytic foundations of various research streams.
Collaborate with researchers to locate data sources used in models and results, grasp data context, and ensure relevance to analyses.
Create and document efficient, replicable methods for data extraction, code development, data source organization, data quality management, and explanation of analytic processes.
Follow protocols to identify dataset or process problems, rectify issues, and systematize data for future analyses.
Transform and format datasets for ongoing analyses; perform quality checks; standardize inputs from surveys, vital registration systems, administrative records, and scientific literature.
Resolve computational and analytic challenges by exploring the data, clarifying questions, and proposing alternative measurement strategies.
Execute code solutions to address analytic questions, diagnose results, and test and evaluate new methods.
Maintain, update, and execute routine yet complex computational processes and statistical modeling central to estimating key indicators.
Maintain reproducible analytic pipelines in Python with Git version control.
Address intricate questions to support senior researchers and external requests from collaborators, media, policymakers, donors, and other stakeholders.
Leverage emerging technologies to optimize data workflows, enhance analysis and visualization, and assist with routine data quality checks and issue identification.