Research Scientist, Computational Oncology

Research Scientist, Computational Oncology

Sage Bionetworks is currently recruiting a research scientist with a strong background in statistical modeling, machine learning, and data analysis, particularly as applied to large-scale biological and/or cancer genomics data sets. We are active in a number of research areas including immuno-oncology, spatial-temporal tumor heterogeneity, and drug response which we study using state-of-the-art molecular, imaging, and clinical modalities. The research scientist will contribute to the DREAM Challenges – i.e., community-wide competitions – as well as large-scale consortia including the Human Tumor Atlas Network (HTAN), the Cancer Systems Biology Consortium (CSBC), and the Physical Sciences Oncology Network (PS-ON).

What you’ll be doing:

  • Conduct analysis on high-dimensional biological (e.g., genomic, transcriptomic, imaging) data.
  • Create workflows to facilitate collaboration across consortia (e.g., Dockerize computational methods).
  • Participate in and/or lead scientifically-engaged working groups within the HTAN, CSBC, and/or PS-ON consortia.
  • Present results concisely and effectively to collaborators.

Example projects include:

  • Implement a pipeline to compare existing image analysis algorithms across benchmark datasets.
  • Develop or apply consensus clustering approaches to define expression signatures correlated with patient outcome.
  • Model drug response using genomic, transcriptomic, and clinical features.
  • Validate methylation markers of risk computationally.

We’d love to hear from you if you:

  • Have a PhD in statistics, mathematics, physics, computational biology, computer science, bioinformatics, or related quantitative discipline.

OR

  • Have a Masters degree in one of the above areas, 5+ years of significant relevant work experience, and a strong track record of statistical analysis and/or machine learning.
  • Area  proven expert  in state-of-the-art machine learning and statistical techniques, such as modeling (e.g., regularized regression, survival analysis, GLM), supervised learning (e.g., SVMs, neural networks), unsupervised learning (e.g., k-means), dimensionality reduction (e.g., PCA), and Bayesian analysis.
  • Have exceptional problem-solving skills, particularly the ability to address a defined problem or hypothesis (biological or otherwise) creatively and with limited supervision.
  • Have strong programming skills in R and/or Python.

Additional Skills/Preferences

  • Experience working with high-dimensional biological data, such as gene expression, genomic, imaging, drug response, flow cytometry, or CyTOF.  Immediate needs and emphasis are in RNA-seq, single-cell RNA-seq, single-cell imaging, video, and drug response data.
  • Knowledge of biology, particularly cancer.
  • Demonstrated excellence in research.
  • Software development skills, including experience with version control software (e.g., github).
  • Familiarity with cloud environments (especially AWS) and containerization approaches (principally Docker).
  • A passion for open-access innovation.
  • Strong collaboration, teamwork, presentation, and communication skills.

About Sage Bionetworks

Sage Bionetworks is a world-leading nonprofit biomedical research organization. We are dedicated to building and supporting open communities of collaborative research in human health and genomics. We are developing multiple initiatives designed to facilitate scientific collaborations and enable direct contributions of ideas and data from citizens to research projects.

Sage embraces diversity and equity. We offer a comprehensive benefits package, including relocation benefits, to bring the right talent to the team. We are based in Seattle, WA, and collaborate broadly throughout the world.

Apply here.


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.