mHealth (Digital Health) Analytics Intern/Co-Op

mHealth (Digital Health) Analytics Intern/Co-Op

Sage Bionetworks is currently looking to fill an internship position in our mobile health analytics group. We are actively running large scale mobile based clinical studies that use wearable sensors and smartphones to quantify symptoms of disease and health. If you are looking to build a career in health informatics and want to be part of the mobile health revolution, we would love to hear from you!

Position Overview
The intern will be mentored by staff scientists at Sage Bionetworks, with opportunities to contribute to active mobile health studies. Benefits of interning at Sage include opportunities to work on unique mobile health datasets and scientific projects, learn professional data science and software engineering skills in the context of mobile health, while working on open source projects and engaging in open science. You will analyze real time data streaming from thousands of participant phones and help us design the next generation of research and health applications.

Specific Responsibilities Include:

  • Process and integrate remote sensor data using cloud computing.
  • Prototype and test new services for mHealth data including visualization, documentation, and computation.
  • Perform signal processing and feature extraction from sensor data.
  • Apply supervised and unsupervised machine learning methods to quantify disease.
  • Automate the execution of new analysis methods using scripting and statistical programming

Basic Qualifications:

  • Ability to commit to a minimum of 6-months with a possibility of extension and/or conversion to a full-time position.
  • Enrollment in an accredited degree program working towards a degree in bioinformatics, statistics, electrical engineering or a closely-related discipline.
  • Fluent programming skills in R, Python or Matlab.
  • Experience with computing in a Unix/Linux environment.

Additional Skills/Preferences:

  • Graduate students are strongly preferred; exceptional junior- or senior-level undergraduates may be considered.
  • Strong verbal, written, and organizational skills.
  • Prior demonstrable experience through coursework or projects in data science, algorithms, remote sensing, signal processing and statistics
  • Experience with analyzing time series data in measuring biological processes such as accelerometers, force plates, and heart rate monitors is a plus.

About Sage Bionetworks
Sage Bionetworks is a Seattle, WA based non-profit organization dedicated to advancing biomedical research through the implementation of open and reproducible science. Using cutting edge machine-learning methodologies, in collaboration with scientists around the world, we build predictive models of disease-related phenotypes through integrative analysis of large-scale genomic and imaging data sets. To enhance collaborative efforts, we provide a collaborative compute platform ( for sharing research insights in a transparent, reproducible fashion.

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Current Positions

Computational Oncology

The Computational Biology group focuses on developing integrative probabilistic models for prediction of disease phenotypes and validating of hypotheses generated by novel methodologies. Currently opportunities include: positions in Oncology focused on conducting original research in analyzing large-scale high dimensional genomics data to develop predictive models of cancer phenotypes. Positions in collaboration with the recently merged Sage/DREAM effort, focused on designing and implementing crowd-sourced collaborative challenges around cancer phenotype prediction problems. Positions in stem cell bioinformatics with a focus on development of the data and analysis bioinformatics portal for the Progenitor Cell Biology Consortium, as well as research projects on modeling molecular mechanisms underlying stem cell differentiation.

Digital Health

Sage Bionetworks’ digital health program is designed to improve disease characterization through the use of sensor-based technologies and bi-directional feedback to improve health monitoring and provide quantitative metrics to assess disease impact on health and on quality of life. We maximize the insights gained from these efforts by providing them through Synapse, a collaborative compute platform. Our mHealth team includes expertise in software engineering (both iOS and Android), clinical study design and development, data governance and data analysis. We are actively involved in projects across a range of disease areas and within the Precision Medicine Initiative.