Data Analyst - Healthcare & Behavioral Research

Responsibilities - 

Your work will span the following areas - 

- Trend Analysis: Analyze monitoring data and work with operations and design teams to uncover trends in program quality and outcomes.

- Impact Analysis: use methods from econometrics and epidemiology to identify counterfactuals and understand impact of program and variants.

- Exploratory Analysis: data mine diverse types of data to help decide which state partner, health condition area to target in our expansion.

- Product and Services Improvement: identify levers for behavior change and complications reduction and influence design pipeline through data

- Collaborate with researchers from Stanford, Harvard, Ariadne Labs and others to carry out implementation research, cost-effectiveness analyses.

- Lead writing the quantitative methods sections of manuscripts submitted to partner-Governments, funders and potential stakeholders.

- Manage all aspects of the data pipeline and innovate and streamline data collection and database management

- Research best systems and practices for improving current monitoring system quality

- Develop dynamic dashboards or reports and visualizations for internal/external sharing.

Desired Qualifications - 

- MPhil. PhD or equivalent graduate level qualification. Masters applicants are encouraged to apply and will be considered on a case-to-case basis.

- Coursework in computer science, statistics, math, econometrics or other heavily quantitative analytical discipline applied to health or social sector.

- 2+ years of working experience, which can be waived in favor of multiple years of research experience at post-graduate level

- Experience wrangling and analyzing health or social sciences data

- Excellent communication skills- you- ve either published peer-reviewed papers or written widely disseminated technical reports

- Excellent applied statistics skills, such as distributions, statistical testing, time-series, difference-in-difference, regression analyses, etc

- Experience with data science toolkits (R, Stata, or SAS), visualization software or languages

- Experience with Shiny, Tableau, or other dashboard/report making tools

- Has done innovative analysis on routine data or worked with challenging data