About Roche Diagnostics
Roche is a global pioneer in pharmaceuticals and diagnostics focused on advancing science to improve people’s lives. The combined strengths of pharmaceuticals and diagnostics under one roof have made Roche the leader in personalized healthcare – a strategy that aims to fit the right treatment to each patient in the best way possible. Roche is the world’s largest biotech company, with truly differentiated medicines in oncology, immunology, infectious diseases, ophthalmology and diseases of the central nervous system. Roche is also the world leader in in vitro diagnostics and tissue-based cancer diagnostics, and a frontrunner in diabetes management.
At Roche, we believe the best solutions come by gaining different perspectives, asking and answering hard questions and challenging the status quo to develop small ideas and achieve big wins in innovation. Our success in creating a truly diverse and inclusive workplace will directly impact our ability to deliver on our purpose of “Doing Now What Patients Need Next.”
We seek a self-motivated, independent, and creative intern to support computational research for our Reverse Translation – Oncology Bioinformatics team. We have a team of skilled bioinformatics researchers working in a highly collaborative environment with multiple internal and external teams. The work will be focused on implementing established and novel methods for analyzing biological data, including but not limited to single-cell RNA and single-nuclei RNA sequencing data of breast tumors.
The intern will identify, integrate, and analyze publicly available scRNAseq datasets from patients with estrogen receptor positive (ER+), HER2-positive, and triple negative breast cancer, to validate findings from internal studies regarding mechanisms of response and resistance to endocrine therapy. Additional potential projects may include leveraging internal clinical data to explore new mechanisms of resistance to treatment in ER+ breast cancer.
The intern will work closely with researchers in the scRNAseq working group and will attend and present at meetings and journal clubs.
Qualifications:
- The successful candidate will bring strong analytical and problem-solving abilities as well as good communication skills.
- Computational experience (competence with R) is required.
- Prior experience analyzing single-cell RNAseq datasets is preferred.
- The successful candidate will be expected to perform data acquisition and/or analysis independently, iterate and devise new approaches when problems are encountered, and interact with colleagues to evaluate and interpret findings.
- The successful candidate will demonstrate a strong interest in learning about ER+ breast cancer and mechanisms of resistance to endocrine therapy.
