Postdoctoral Fellow in Biological Data Science
Postdoctoral Fellow in Biological Data Science
The Parker Institute for Cancer Immunotherapy (PICI) is radically changing the way cancer research is done. Founded in 2016 through a $250 million gift from Silicon Valley entrepreneur and philanthropist Sean Parker, the San Francisco-based nonprofit is an unprecedented collaboration between the country’s leading immunotherapy researchers and cancer centers, including Memorial Sloan Kettering Cancer Center, Stanford Medicine, the University of California, Los Angeles, the University of California, San Francisco, the University of Pennsylvania and The University of Texas MD Anderson Cancer Center. The institute also supports top researchers at other institutions, including City of Hope, Dana-Farber Cancer Institute, Fred Hutchinson Cancer Research Center, Icahn School of Medicine at Mount Sinai, Institute for Systems Biology and Washington University School of Medicine in St. Louis.
By forging alliances with academic, industry and nonprofit partners, PICI makes big bets on bold research to fulfill its mission: to accelerate the development of breakthrough immune therapies to turn all cancers into curable diseases.
Help us create a world that doesn’t fear cancer. Join us. www.parkerici.org
Overview of the Role
The Fellow will lead the analysis and scientific work for a project studying the mechanisms of resistance to anti-PD-1 checkpoint inhibitor therapy, a widely used type of immunotherapy. This project focuses on high-dimensional imaging of tumor samples to visualize the tumor microenvironment and the immune populations infiltrating or surrounding the tumor. Using PICI’s CANDEL data platform, the Fellow will perform primary processing of molecular data and then marry molecular and clinical data to uncover novel insights. The goal of this analysis work will be to uncover mechanisms of anti-PD-1 resistance, and these insights will directly feed into PICI’s research and clinical programs, making an impact on patients.
The ideal candidate will be a strong scientific thinker, capable of leading the scientific direction of this project. Additionally, they should be a strong technical contributor, capable of working independently to analyze data and produce high-quality results. They will need to balance a mindset of scientific inquiry with a results-driven attitude suited to the quick pace of clinical trial delivery. Lastly, they should be a team player and quick learner, excited to learn to use new analysis tools as well as contribute to shared code.
Reporting Structure and Team
The Fellow reports to the Associate Director of Informatics and is a key member of the Informatics team. The Fellow will also work closely with the Translational Medicine and Research teams. The role will be offered as remote until the staff returns to the San Francisco office post COVID.
FLSA Status: Exempt
Essential Job Functions
- Ingest and process high dimensional imaging data using PICI’s established data processing and storage platform
- Use R to explore data with a variety of methods, looking for associations between molecular features and clinical variables such as response, adverse events, or course of treatment
- Contribute to a shared codebase of high-quality R code for repeatable molecular and clinical data analysis
- Work closely with Translational Medicine group to determine questions of interest and refine results
- Present results to stakeholders, including both internal and external meetings
- Prepare content for conference presentations and publications
Knowledge, Skills, and Experience
- PhD in Bioinformatics, Statistics, Biology, or related discipline
- Publication record in immuno-oncology, translational medicine, immunology, cancer biology, or related field
- 5+ years’ experience analyzing data in an academic or industry setting
- Highly desired: Experience analyzing multiplex imaging data. Exceptional candidates with no imaging experience but other relevant scientific experience will be considered.
- Strong programming skills in R or Python (R strongly preferred)
- Experience with ggplot2 or related visualization libraries
- Knowledge of statistical concepts relevant to translational research (hypothesis testing, survival analysis, regression, etc.)
- Ability to communicate results to a variety of stakeholders, including technical and non-technical scientific audiences
- Ability to work as a team player, respecting others and holding a “learner not knower” attitude
- Ability to work independently in a multi-disciplinary environment
- Bonus qualifications:
- Experience at the bench running imaging assays
- Expert knowledge of R
- Experience with command line tools, cloud computing, and database technologies
- Experience with clinical research