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Open Postdoctoral position, faculty mentor Nigam Shah

Important Info

Faculty Sponsor (Last, First Name): 
Shah, Nigam
Stanford Departments and Centers: 
Biomedical Informatics
Postdoc Appointment Term: 
Two years
Appointment Start Date: 
September 2020

Data science fellow
About us: We are a group of about twenty doctors, engineers, informatics professionals and students focused on enabling better care using existing health data. We develop novel methods to learn from patient-level health data including structured health encounter records, clinical notes, insurance claims, diagnostic imaging, and clinical trial data. A major research thrust is to answer clinical questions that enable better medical decisions using electronic health records (EHRs) and insurance claims data, via a consultation service that uses aggregate patient data at the point of care (https://shahlab.stanford.edu/greenbutton). We also have an active research program to research safe, ethical, and cost-effective strategies for predictive models to guide mitigating care actions (https://shahlab.stanford.edu/paihc). Our research group is part of the Department of Medicine at Stanford.
About the position: The primary research focus of this postdoctoral scholar position is flexible, and will involve developing and advancing methods for data-driven bedside decision making, or machine learning for health system-level improvements. An additional operational focus will involve being responsible for our group’s data analysis and compute infrastructure, including managing dataset ingestion and cleaning, database management, and supporting the cloud compute needs of our lab members. The position offers the opportunity to work with leading Stanford faculty in Informatics, Statistics, and Medicine as well as an opportunity to build real-world credibility in DataOps.
About you: You are a hands-on team member with research experience who collaborates with medical doctors, statisticians and computer scientists to advance methods for clinical decision making using observational health data and/or improving patient care via machine learning. You will manage our data and compute infrastructure to turbo-charge the research of our entire team. You are a contributor at all levels: designing methods as well as experiments to evaluate them, implementing robust code, and coordinating with our collaborators.
You will find this position to be a good fit if you:

  • are passionate about improving health care
  • can set up a GCP instance in your sleep
  • are excited to work with rich, sometimes messy, patient-level data
  • thrive in dynamic, fast-paced environments

You look forward to responsibilities that include:

  • developing and evaluating informatics methods to derive actionable findings from healthcare data of millions of patients
  • pushing the limits on what relational databases can handle
  • healthcare data wrangling including dataset cleaning and access infrastructure
  • producing scalable, reusable code
  • writing manuscripts and progress reports about your research
  • working in a team of researchers

You meet all of the following requirements:

  • PhD in CS, medical informatics, bioinformatics or related quantitative discipline
  • experience in processing and analyzing large datasets
  • 3+ years database management experience
  • 3+ years of coding experience
  • fluency in linux system administration
  • excellent written and oral communication in English, with at least one peer-reviewed first-author manuscript

You meet some of the desired qualifications:

  • 1+ years of experience analyzing health data, such as insurance claims and EHRs
  • knowledge of best practices in data mining and machine learning
  • app and/or Web development
  • GCP system administration

Interested? Please contact: acallaha [at] stanford [dot] edu

Required Application Materials: 

Cover letter, CV, 2+ references

 

Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.