22 Apr
Customer Success Manager (Biotech)
Vacancy expired!
This Jobot Job is hosted by: Emily OlingerAre you a fit? Easy Apply now by clicking the "Apply Now" button and sending us your resume.Salary: $70,000 - $110,000 per year
A bit about us:We are a leading cloud based SaaS startup in the biotech industry. Our platforms leads to insights for improved diagnostics, targeted therapies, and better patient care. We recently completed a $100 million financing round to advance our growth globally to further serve leading healthcare and life science organizations! If you are interested in becoming a leader in the Biotech space, apply today! Location: Mountain View, CA or FULLY REMOTEWhy join us?- 100% of health premiums paid by employer for YOU, 75% paid for dependents
- FSA
- 401K and Stock options
- PTO/Vacations
- Great company culture
- Remote options
- Rapidly growing company
- Establish long term relationships with our clients.
- Support your clients as they transition from sales prospects to active users of our products.
- Expand client accounts, increase client retention, solve client issues, and drive client satisfaction.
- Client mentorship.
- Anticipate potential problems
- 5+ years in customer success
- Strong presentational and organizational skills
- Leadership experience
- Experience collecting requirements, designing solutions and creating documentation
- Experience working directly with clients and partners in the life sciences and clinical industries.
- Technical degree in Biomedical Data Management, Data Science, Bioinformatics, Computational Biology, etc
- Knowledge of program languages such as Python, R, Bash (preferred)
- Understanding of existing techniques for managing and analyzing genomic, clinical/phenotypic, pharmacokinetic, and other molecular data (transcriptomic, metabolomic, proteomic, microbiome) (preferred)
- An understanding of human genetics and the effects on human diseases, e.g. oncology, immunology, cardiovascular (preferred)
- Understanding of statistical models used in translational informatics including linear and logistic regression, linear mixed models, and collapsing analysis.
Vacancy expired!