Postdoctoral Fellow in Biological Data Science (#0120)
- Parker Institution for Cancer Immunotherapy
- Location: San Francisco, CA
- Job Number: 7080563 (Ref #: #0120)
- Posting Date: Jun 30, 2021
- Application Deadline: Open Until Filled
Job DescriptionAbout Us
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 in Biological Data Science will work alongside scientists on the informatics and research teams to make contributions to PICI’s project, BRUCE, aimed at understanding the structure and composition of the tumor micro-environment in brain cancers. A crucial component of this research is the integration of existing multi-omic brain cancer data sets (e.g. genome sequencing, gene expression etc.) with novel imaging datasets that will be generated as part of the BRUCE project.
The Fellow will lead the analysis and scientific work for a project focused on understanding the structure and composition of the tumor micro-environment in brain cancers. This project focuses on high-dimensional protein image analysis of tumor samples to visualize the tumor antigen expression, and the immune populations present in the tumor microenvironment. Using PICI’s CANDEL data platform, the Fellow will perform primary processing of sample matched molecular data and then marry molecular and clinical data to uncover novel insights. The fellow will also identify, incorporate and analyze additional multi-omic datasets of brain tumors in the field to expand the learnings of the project, position the findings in the rapidly developing field of novel datasets. The goal of this analysis work will be to identify targets for novel immunotherapeutic discovery, clinical combinations and trial design. 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, significant contributor to the multi-functional consortia, and a 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 Director of Informatics, with a dotted line reporting to the Senior Director of Research and is a key member of the Informatics team. The Fellow will also work closely with the Translational Medicine teams and PICI member academic labs and will supervise and mentor 1-2 junior scientists.
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 the Research group and Academic investigators to determine questions of interest and refine results
• Be a key scientific contributor and team member of the Brain tumor program
• 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, neuroscience 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:
o Experience at the bench running imaging assays
o Experience analyzing single cell transcriptomic, TCR sequence data and applying to antigen discovery
o Integrating proteomic and molecular datasets
o Expert knowledge of R
o Experience with command line tools, cloud computing, and database technologies
o Experience with clinical research
o Mentoring junior data scientists