Senior Scientist

Palo Alto, California, United States expand job description ↓

Description

Do you want to develop prenatal diagnostics that can affect the lives of millions of expecting parents? BillionToOne (Y Combinator S17) is looking for a quantitative scientist or bioengineer to develop our product pipeline. As part of our technology platform, we have repurposed decades-old Sanger sequencing to generate quantitative molecular data that match next-generation sequencing within less than 1% CV. This has incredible implications for decreasing the cost of molecular genetics tests and making them available and affordable across the world.


You will own R&D for our Sanger-based sequencing platform and translate our proprietary molecular counting technologies to novel clinical diagnostics, with tremendous potential to bring prenatal testing to the world. This is an extremely impactful position, appropriate for an inspired scientist and extraordinary problem solver.


Responsibilities
  • Learn, use and modify our custom R/Python code for experimental design purposes, and complement them with bioinformatic tools as needed to design probes for diagnostic tests
  • Plan and develop the research strategy
  • Design and perform experiments to validate diagnostic/screening assays
  • Analyze experiments and troubleshoot them as necessary
  • Read the literature from chemistry and molecular biology to synthetic biology, bioengineering, and statistical learning fields to find creative opportunities for further improvements in our assays.
  • Mathematically/computationally model experimental processes to iterate on the research strategy and assay development

Requirements

Who You Are:

  • PhD in biological sciences, bioengineering, biophysics or related discipline with molecular biology lab experience
  • A deep quantitative understanding of and approach to biology
  • A strong background in physics, applied mathematics or mathematical biology (i.e., you intuitively understand concepts such as error propagation, probability distributions, Markov models, Monte Carlo simulations, phase diagrams etc.)
  • Ability to analyze data and model biological processes in R or Python
  • Knowledge of data structures and algorithms and familiarity with industry tools (Github, command line, AWS)

Nice-to-haves:

  • Next-generation-sequencing experience
  • A deep understanding of or experience with nucleic acid chemistry
  • Previous industry or startup experience

More About Us:

BillionToOne detects genetic disorders in the baby through a simple blood test of the mother. Our first test for beta-thalassemia and sickle cell disease is already in clinical trials. These are the most common genetic disorders in the world, and their prenatal detection currently requires invasive methods with high miscarriage risk. More than 100 million people are carriers for these disorders. By being able to detect both large chromosomal disorders as well as single gene disorders at much lower costs, we aim to make prenatal genetic testing available for all, including in developing countries.


BillionToOne has raised funding from institutional funds and VCs such as Y Combinator, Fifty Years, Uphonest Capital, Civilization Ventures as well as from prominent angel investors who previously invested in SpaceX, Box, and Palantir.

Benefits

We offer highly competitive salary and benefits combined with a truly generous equity package. You will be working on interesting problems with extremely high-impact.

BillionToOne is an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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Application Details
PhD in biological sciences, bioengineering, biophysics or related discipline
Molecular biology lab experience
A deep quantitative understanding of and approach to biology
A strong background in physics, applied mathematics or mathematical biology (i.e., you intuitively understand concepts such as error propagation, probability distributions, Markov models, Monte Carlo simulations, phase diagrams etc.)
Ability to analyze data and model biological processes in R or Python
Knowledge of data structures and algorithms (e.g., sets, hash tables etc.) and familiarity with CS tools (Github, command line, AWS etc.)