Dana-Farber Cancer Institute seeks to advance precision cancer medicine by analyzing cell-free DNA and epigenomic datasets from patient samples to drive discovery of diagnostic and predictive biomarkers.
Requirements
- Knowledge of UNIX/Linux.
- Familiarity with scripting in Python.
- Familiarity with statistical programming using R.
- Familiarity with principles of experimental design and modern data analysis paradigms.
- Experience with machine learning algorithms for biomarker discovery.
- Ability to analyze fragmentation patterns of DNA circulating in plasma and correlate with transcriptional programs in cancer.
- Experience with deep learning techniques for algorithm development.
Responsibilities
- Develops new algorithms for the analysis of epigenomic datasets derived from clinical specimens, including tissues and blood.
- Integrates existing tools and databases into high-throughput analytical pipelines.
- Builds data processing pipelines to convert raw data into formats compatible with conventional statistical analysis and visualization under supervision of mentor.
- Follows a detailed manual to perform routine data analysis.
- Monitors, downloads, organizes, and manages data from public data repositories or generated by collaborators.
- Helps prepare tables and figures for manuscript preparation.
- Trains to develop timelines and components for multiple routine projects; masters multi-tasking so that complex projects involving many interdisciplinary individuals move forward smoothly.
Other
- Bachelor's degree required in a STEM field.
- Ability to discuss and present results, share ideas accurately and communicate them effectively, both in writing and verbally with supervisory oversight.
- Strong interpersonal skills – ability to effectively interact with all levels of staff and external contacts.
- Excellent analytical, organizational and time management skills.
- Ability to work in a collaborative, multidisciplinary environment with oncologists and cancer biologists.
