Computational Biologist

Organization
Freenome
Job Location
201 Gateway Blvd
South San Francisco, CA 94080
Benefits

health / death / vision insurance

flexible PTO

commuter benefits

catered lunch

 

 

 

Job Description

About the Role
As a computational biologist at Freenome, you will be an integral part of our R&D team, working in collaboration with machine learning specialists to develop the core analysis technology driving Freenome’s mission of early detection and intervention in human disease. You will also work closely with our molecular biologists to evaluate new laboratory methods that can drive improvements in end-to-end assay performance.

 

Responsibilities

    • Participate in cutting edge research in statistical modeling and inference of biology (including cancer research, genomics, computational biology/bioinformatics, immunology, therapeutics, and more)
    • Utilize and code optimal methods to solve real world, large scale health problems
Requirements

 

  • PhD (or equivalent research experience) in computational biology, bioinformatics, cancer biology, genomics, or a related field
  • Extensive experience working with next-generation sequencing data. You’ve analyzed raw reads to test scientific questions
  • Proficiency in at least one general-purpose programming language: Python, Java, C++, etc
  • Proficiency in a scientific data processing ecosystem: R, Python/Numpy, etc
  • Basic knowledge of statistics: significance testing (and the hazards therein), Bayes’ theorem, properties of basic probability distributions used in computational biology
  • Skilled at clearly communicating scientific results with a team and working collaboratively towards next steps



Above The Average

  • 3+ years postdoctoral research in industry or academia and demonstrated success in leading research and development projects

  • Expertise in oncology, immunology, developmental biology, or similar applied fields

  • Experience working with NGS data beyond diploid germline DNA-seq: RNA-seq, single-cell sequencing, metagenomics, epigenomics, etc

  • Experience working with other digital biological data: mass spec, images, EHR/EMR, etc

  • Bench experience: you’ve analyzed data you generated yourself

  • Applied experience in machine learning: cross-validation, parametric and nonparametric  regression, hierarchical modeling, ensemble learning, neural networks, and graphical models

  • Experience with software engineering best practices: code quality, testing, performance optimization, development of research tools infrastructure

About Our Organization

Freenome’s Culture
Our culture is simple. We value Empathy, Trust and Integrity. We feel from the perspectives of each other, patients and the communities we serve. We give each others the benefit of the doubt and believe we’re all working as a team towards our goals. We conduct ourselves with integrity, empowering others to grow in a collaborative environment. Freenome explicitly  prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.

 You can prioritize, manage and execute your goals and align them to the mission of Freenome. You’re a steward of the culture and hold yourself and the team accountable. You believe hiring and mentorship are fundamental to building the organization. You bring ownership to your role, focus on your process and find the gaps to help Freenome reach its full potential. You welcome feedback and criticism knowing its value is to build people up and support each other, rather than tearing each other down. 

Students whose classmates are interested in science are more likely to think about a career in science, technology, engineering, and mathematics, a new study says.

CNBC reports that the genetic counseling field is expected to grow as personalized medicine becomes more common.

Gladys Kong writes at Fortune that her STEM background has helped her as a CEO.

Social scientists report that the image of the 'lone scientist' might be deterring US students from STEM careers.