Data Manager at Stanford School of Medicine
Employer: Stanford School of Medicine – School of Medicine
We enter the world with a set of genes that determine many factors of our health. From that moment forward, every aspect of our lives affects our health. There is unequivocal evidence that social, environmental, and behavioral factors are by far and away the strongest predictors of health and disease. Founded in 2015, the Stanford Center for Population Health Sciences’ (PHS) mission is to improve population health by bringing together diverse disciplines and data to better understand and address the social and environmental determinants of health.One of PHS’ strategic goals is to spur and support cutting-edge transdisciplinary population research that advances our mission. Toward this end, we have created a world-class data ecosystem that hosts over 100 datasets, supporting 800 researchers and ~1,500 research projects. We have also established a large portfolio of exciting research and data partnerships around the globe and working groups focused on society’s most pressing health problems (e.g., air pollution and health, racial equity, mental health).PHS’ data team is responsible for managing PHS’ data ecosystem, advancing critical research projects and initiatives, developing novel data products and services, and acquiring new datasets that stimulate population health research. The Data Manager will work in collaboration with PHS’ faculty directors and senior team to advance these efforts. Examples of responsibilities include:Supporting key research partnerships (e.g., Solano County Health Department, American Board of Family Medicine, Tata Trusts, Born in Bradford) and projects. We are currently working with our partners on a number of projects related to COVID-19 and health equity. The position will support studies looking at the long term impacts of COVID-19, particularly on underserved communities and how the pandemic has exacerbated or alleviated existing social and environmental determinants of health.Assisting with the development of innovative data products and mechanisms for conducting data overlay and linkage with existing datasets and testing new research tools that will be shared with the broader research communityManaging a portfolio of PHS’ large high-risk datasets (e.g., large claims and electronic health record datasets) available to Stanford researchers.PHS works in close collaboration with experts from across the university (e.g., Stanford Research Computing, Stanford Center for Biomedical Informatics Research, Department of Computer Science) and external partners (e.g., Google, Facebook). This is an excellent opportunity to learn from leading experts in the field while contributing to the advancement of health equity.Excellent verbal and written communication skills are desired in order to provide feedback to members of the scientific community to facilitate the description of data, suggested analytic approaches and effectively provide documentation and specifications to co-workers during research development, and contribute the drafting of scientific literature and grant applications using results from analyses.Previous experience working with large, complex health (EHR or claims) datasets is desired. The work will take place in a dynamic environment where specifications often change rapidly in response to demand, so the candidate must be able to be flexible. The successful candidate will work at Stanford University in Palo Alto, California. Complies with and supports University and government health and safety regulations and policies. Candidates with a background and/or interest in research on health, equity, or underserved communities encouraged to apply.* – Other duties may also be assignedDESIRED QUALIFICATIONS:experience working with large datasetsexperience working with health care dataEDUCATION & EXPERIENCE (REQUIRED): Master’s degree in biostatistics, statistics or related field.KNOWLEDGE, SKILLS AND ABILITIES (REQUIRED):Proficient in at least two of R, SAS, SPSS, or STATA.Skills in descriptive analysis, modeling of data, and graphic interfaces.Outstanding ability to communicate technical information to both technical and non-technical audiences.