The Institute for Health Metrics and Evaluation (IHME) is an independent research center at the University of Washington. Its mission is to deliver to the world timely, relevant, and scientifically valid evidence to improve health policy and practice. IHME carries out its mission through a range of projects within different research areas including: the Global Burden of Diseases, Injuries, and Risk Factors; Future Health Scenarios; Costs and Cost Effectiveness; Local Burden of Disease; Resource Tracking; and Impact Evaluations. Our vision is to provide policymakers, donors, and researchers with the highest-quality quantitative evidence base so all people live long lives in full health.
IHME is committed to providing the evidence base necessary to help solve the world’s most important health problems. This requires creativity and innovation, which is cultivated by an inclusive, diverse, and equitable environment that respects and appreciates differences, embraces collaboration, and invites the voices of all IHME team members.
IHME has an exciting opportunity for a Data Analyst on the Data Intake Team for the COVID Research Project. The Covid Research Project’s aim is to provide policymakers, donors, and researchers with the highest-quality quantitative evidence base to make decisions that achieve better health. The data intake team extracts, processes, visualizes, and reports on various data inputs required for the global COVID-19 modeling effort. Key data types include epidemiological surveillance of cases and deaths, hospitalizations, policies for social distancing, mobility patterns, covid testing data, mask use, information on person-to-person contacts, among others. The team is agile to process new data types as they become available and relevant.
The main purpose of these positions is to provide support to key research projects through data extraction and formatting, database management, data quality management, computational support to multi-disciplinary research projects, and providing key inputs for papers and presentations. Data Analysts must develop an understanding of different research needs and analytic functions across multiple projects to best meet researcher needs. Data Analysts must be able to independently translate requests into actionable results through interactions with research databases, formulation of displays of results, and development of complex code to be applied to a variety of quantitative data.
A Data Analyst must develop an understanding of different research needs and analytic functions across multiple projects to best meet researcher needs. The Data Analyst must be able to independently translate requests into actionable results through interactions with research databases, formulation of displays of results, and development of complex code to be applied to a variety of quantitative data. The position calls for dexterity working with complex databases and the ability to assess, transform, and utilize quantitative data using multiple coding languages such (R, Python, SQL). The individual must then quality control results to ensure that other team members have exactly what they need to incorporate the data and results into their own components of the analytic process, presentations, and papers.
Additionally, this position will work alongside other Data Analysts on complementary projects and will require knowledge and skill sharing and collective problem solving. Overall, the Data Analyst will be a critical member of an agile, dynamic research team. This position is contingent on project funding availability.
- 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.
- Data management and analytics
- Extract data manually and wherever possible through code-based solutions and format data into required templates for use in modeling efforts.
- Problem-solve computational and analytic challenges by investigating the data, understanding the root questions, and coming up with alternative measurement strategies.
- Implement code solutions in order to answer analytic questions, perform diagnostics on results, and test and assess new methods.
- Maintain, update, and adapt databases containing health data from multiple sources such as surveys, vital registration systems, administrative records, and published studies relevant to demographic estimation
- Maintain, update, and carry out routine but complex computational processes and statistical modeling that are central to generating estimates of key indicators.
- Execute queries on databases and resolve intricate questions in order to respond to the needs of senior researchers and external requests from collaborators, media, policymakers, donors, and other stakeholders.
- Bring together data, analytic engines, and data visualizations in one seamless computational process.
- Use protocols to identify problems with datasets and routine computational processes, rectify issues, and systematize data for future analyses
- Transform and format data sets for use in ongoing analyses. Catalogue and incorporate these datasets into databases. Perform quality checks.
- Create tables, figures, and charts for presentations and publications.
- Provide referencing and other support for publications and presentations.
- Communicate clearly and effectively while contributing as a member of both the Institute.
- Work closely with other team members to assist with relevant tasks, facilitate learning new skills, and to help resolve emerging problems on different projects.
- Participate in overall community of the Institute, carrying out duties as required as team members with other Institute members
Bachelor's Degree in social sciences, engineering, computer science or related field plus two years' related experience or equivalent combination of education and experience.
- Demonstrated success in developing code in R, Python, SQL, or other coding language.
- 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.
- 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 to apply analytic skills to better understand complex information in a systematic way.
- Strong quantitative aptitude.
Equivalent education/experience will substitute for all minimum qualifications except when there are legal requirements, such as a license/certification/registration.
Conditions of Employment: Weekend and evening work sometimes required.