Data Analyst (Temporary)

University of Washington•Washington, DC

3d•$78,324 - $90,072•Remote

About The Position

IHME has an outstanding opportunity for a Data Analyst to join the Simulation Science team. Reporting to the Project Officer for the Simulation Science team, the Data Analyst is responsible for turning complex health data into actionable insights. This position supports key research projects by managing and shaping large datasets, ensuring data quality, providing computational support, extracting and formatting data, and providing key inputs for publications for multidisciplinary research projects. To succeed, Data Analysts must develop a strong understanding of diverse research priorities and analytic approaches across multiple projects, enabling them to adapt solutions that meet each project’s unique needs. They independently translate requests into actionable results, working with research databases, creating clear visualizations, and writing complex code to analyze quantitative data, so that findings can drive meaningful decisions. Success in this role requires agility with complex databases and fluency in Python (pandas, NumPy). Beyond technical expertise, you’ll ensure accuracy through rigorous quality checks and collaborate closely with other analysts to solve problems and share knowledge. As part of IHME’s commitment to innovation, you’ll also use emerging technologies to improve and enhance the research and our overall impact. Overall, the Data Analyst will be a critical member of an agile, dynamic team. IHME is a grant funded organization and this position is contingent on project funding availability. This position currently has funding through June 30, 2027.

Requirements

Bachelor’s degree in social sciences, engineering, computer science, or related field plus two years’ related experience.
Proven experience and proficiency in using Python (pandas, NumPy) for data analysis tasks, including, but not limited to, data cleaning, transformation, visualization, and statistical evaluation.
Demonstrated 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/or ways in which quantitative research and data science can be used to create valuable global public goods.
Experience working with large secondary datasets.
Strong written and spoken communication in English.
Demonstrated self-motivation, ability to absorb detailed information, flexibility, and ability to thrive in a fast-paced, energetic, highly creative, and entrepreneurial environment.
Ability to learn new information quickly and apply analytic skills to better understand complex information in a systematic way.
Strong quantitative aptitude.
Flexible attitude and interest in moving around to a variety of different research teams, getting a broader range of experience, rather than focusing on a particular research area or team.

Nice To Haves

Master’s degree in a relevant quantitative discipline.
Experience with household survey data (DHS, MICS, LSMS, or similar).
Familiarity with simulation modeling, microsimulation, or agent-based modeling.
Experience with Linux compute clusters.
Working knowledge of Git and collaborative software development practices.
Prior public-health or nutrition research experience.

Responsibilities

Become familiar with substantive areas of expertise to understand the dimensions and uses of health data and the analytic underpinnings of different research streams
Work directly with researchers to identify the source of data used in models and results, understand the context of the data, and ensure that they are relevant to the analyses themselves.
Create and document efficient, effective, and replicable methods for extracting data, developing code, organizing data sources, managing data quality, and explaining complex analytic processes.
Use protocols to identify problems with datasets and routine computational processes, rectify issues, and systematize data for future analyses.
Transform and format datasets for use in ongoing analyses; perform quality checks.
Transform and standardize datasets from a wide range of inputs such as surveys, vital registration systems, administrative records, and scientific literature.
Problem-solve computational and analytic challenges by investigating the data, understanding the root questions, and coming up with alternative measurement strategies.
Execute code solutions in order to answer analytic questions, perform diagnostics on results, and test and assess new methods.
Maintain, update, and carry out routine but complex computational processes and statistical modeling that are central to generating estimates of key indicators.
Maintain reproducible analytic pipelines in Python, version-controlled in Git.
Resolve intricate questions in order to respond to the needs of senior researchers and external requests from collaborators, media, policymakers, donors, and other stakeholders.
Use emerging technologies to optimize data workflows, enhance analysis and visualization, and assist with routine data quality checks and issue identification.
Create tables, figures, and charts for concept notes, presentations, publications, and proposals.
Provide referencing and other support for publications and presentations.
Communicate clearly and effectively while contributing as a member of the Institute.
Work closely with other team members to assist with relevant tasks, facilitate learning new skills, and help resolve emerging problems on different projects.
Other duties as assigned.