Verge Genomics



Verge Genomics

San Francisco, CA

Contact Person:
Shannon Stone

Phone, Email, Postal Address:
(603) 801- 9013

How to Apply
Email your resume to

# of Openings: 1


Verge Genomics is looking for a biostatistician to identify gene targets and small molecules that therapeutically reverse neurodegenerative diseases. You will lead projects to ensure the statistical rigor of our ongoing drug screen

Using a combination of simulated and empirical data, you will design experiments and analyze their results to determine the extent to which drugs have significant therapeutic effect in our pre-clinical model systems. Alongside our team of computational biologists, you will help design database architecture and software API to rapidly store and access statistical results from our database and web portal. You will have extensive experience analyzing, mining, and developing visualizations for high dimensional data.


    You will:

  • – Perform statistical analyses and modeling, including design of pre-clinical studies, algorithm development, quality monitoring, and process automation.
  • – Play a central role in our new drug screen studies from conception to completion by ensuring that studies are designed with sufficient power and use appropriate endpoints.
  • – Work with our team of software engineers to automate core statistical analyses and ensure your results are integrated into further downstream analyses.
  • – Play a meaningful role in authoring pre-clinical study reports and other public manuscripts documenting our results.
  • – Be an advocate for good science by remaining abreast of statistical advances in the field and sharing our best statistical work with the outside world by submitting peer-reviewed manuscripts to relevant journals.

Skills / Education Requirements:

  • – MS or PhD with 1-5 years of experience in statistics, mathematics, computer science, computational biology, or related field.
  • – Proven expertise in biostatistical paradigms for drug screen studies, including simulation methods to predict power, models to account for bias, and methods to meta-analyze results from multiple experiments.
  • – Experience with computationally intensive statistical methods, such as Monte Carlo methods, local regression, artificial neural networks, and others.
  • – Demonstrated fluency with R and Python, including knowledge of relevant statistical packages and libraries.
  • – Experience working with large relational datasets, using SQL and basic database design.
  • – Experience with data analysis and software engineering in Linux/Unix/OSX in a cloud-based environment (AWS).
  • – Excellent team communication skills and ability to collaborate across our organization

Operating Systems: R, Python

Date Posted: 10-02-2018