At Sage Bionetworks, we believe that we can learn more by learning from each other. By improving the way scientists collaborate, we help to make science more effective. We partner with researchers, patients, and healthcare innovators to drive collaborative data-driven science to improve health. Making science more open, collaborative, and inclusive ultimately advances biomedicine.
The ubiquity of sensors in consumer devices and wearables offers unprecedented opportunities to collect real-world health information, but interpretation of these data requires the efforts of talented analysts. Do you have the ability to develop statistical and machine learning models to address computational problems in the area of digital and mobile health? Are you the type of person who enjoys engaging with scientists and researchers across many disciplines to help solve important problems through the analysis and modeling of health data? Do you want to be an integral part of a team that includes computational biologists, data scientists, research ethicists, software engineers, and data librarians? If so, you could be our next Bioinformatics analyst in the Mobile Health (mHealth) team. We are looking for talented candidates to bring their expertise in data processing, statistical analysis, machine learning, and human health to extract meaningful insights from data collected from wearables and smart devices.
What you’ll be doing:
- Using quantitative analysis, signal processing, data mining, and machine learning to analyse large amounts of sensor data from smartphones, wearables, and other devices.
- Establishing the biological, clinical, and participant relevance of sensor data collected in digital health studies.
- Contributing to the improvement and automation of the data processing and analysis pipeline across different mobile health studies.
- Help organize public benchmarking efforts (e.g. dreamchallenges.org).
We’d love to hear from you if you:
- Are passionate about open science and collaboration.
- Are passionate about digital health technologies.
- Have a degree in quantitative discipline (e.g., statistics, computational biology, biomedical engineering, biomedical informatics, computer science, applied mathematics, or similar) or another scientific degree with commensurate experience.
- Have a MS with 2 years (or BS with 4 years) of additional experience in an analytical role developing both statistical and machine learning models.
- Are proficient at extracting, cleaning, and analyzing physiological or mobile sensor data sets using data/statistical tools such as R, Python, or similar.
- Have practical knowledge of digital signal processing and analysis of time-series data.
- Have expertise in exploratory and statistical data analysis (such as linear models, multivariate analysis, predictive modeling and stochastic models).
- Familiarity with Deep Learning frameworks (Keras, TensorFlow, PyTorch etc).
- Experience with the analysis of sensor data from wearables or mobile sensors and clinical data.
- Familiarity with software engineering practices and experience developing production software.
- Familiarity with Image analysis.
In light of recent concerns of Covid-19, all interviews will be conducted remotely, and most positions will be remote through at least June 30, 2021. The option to work on-site at our Seattle office prior to June 30, will be considered upon request.