Social Science Research Scholar (1-Year Fixed Term) (Remote Opportunity)

Abundance Network; United States; À distance; USD 90 000 - 140 000; Plein temps

Résumé du poste

Abundance Network in the United States seeks a research scholar to lead quantitative analyses within a large health data program. The role emphasizes causal inference using quasi-experimental designs and aims to publish in high-impact peer-reviewed journals.

Responsibilities include supervising students, writing manuscripts, mentoring junior researchers, and helping prepare grant proposals, with remote work eligibility and an initial 1-year fixed term.

Qualifications

• Doctoral degree with quantitative training or research experience.
• Training and experience in quasi-experimental techniques (regression discontinuity, difference-in-differences, interrupted time-series, etc.).
• A background in health-related research is a plus but not required

Responsabilités

• Carrying out data analyses
• Supervising data analyses led by graduate students and postdoctoral fellows
• Writing manuscripts for publication in scientific journals
• Mentoring and advising junior researchers
• Assisting with, and leading the writing of, grant applications for research funding
• Communicating with research funders, data providers, and project administrative staff

Connaissances

Strong coding skills in R

Strong coding skills in Stata

Formation

Doctoral degree with quantitative training or research experience

Outils

R

Stata

Description du poste

The Division of Primary Care and Population Health seeks to serve our community through caring, learning, and innovation for the whole person through all stages of live. While engaged in practice and teaching, our faculty are dedicated to establishing new knowledge as the basis for future practice and prevention, and to the greatest extent possible applying that knowledge to improve care for all and reduce health disparities.

The research scholar will take a leadership role in Pascal Geldsetzer's research group, which is a \"dry lab\" focused on quantitative analyses of existing, and often publicly accessible, datasets. The group works on a wide variety of research topics, with current foci being the causal effect of shingles vaccination on cognition https://www.medrxiv.org/content/10.1101/2023.05.23.23290253v1, the health effects of sodium intake, and population health issues more generally. Key data sources for projects are large population-based cohort studies, electronic health record data, mortality data, and other large health-related administrative datasets. The group is also working on randomized intervention studies in low- and middle-income country settings and the re-analysis of clinical trial data. The researcher will be expected to publish in high-impact peer-reviewed journals.

We are in particular looking for individuals with in-depth experience in econometric/quasi-experimental approaches for causal effect estimation (e.g., regression discontinuity and difference-in-differences). We are looking for someone to start as soon as possible but there is no specific deadline for the application - we hire on an ongoing basis.

The initial appointment will be 1-year fixed term. The fixed term may be renewed for additional years based on business needs.

This is a remote eligible position.

CORE DUTIES:

• Carrying out data analyses
• Supervising data analyses led by graduate students and postdoctoral fellows
• Writing manuscripts for publication in scientific journals
• Mentoring and advising junior researchers
• Assisting with, and leading the writing of, grant applications for research funding
• Communicating with research funders, data providers, and project administrative staff

EDUCATION & EXPERIENCE:

• Doctoral degree with quantitative training or research experience.
• Training and experience in quasi-experimental techniques (regression discontinuity, difference-in-differences, interrupted time-series, etc.).
• A background in health-related research is a plus but not required

KNOWLEDGE, SKILLS AND ABILITIES:

• Strong coding skills in R, Stata, or other statistical software package.
• Strong writing and analytical skills.
• Ability to prioritize workload.