Chai Discovery

Chai Discovery

Data Ops

San Francisco office

Sponsorship not specifiedDetected 44 days ago
Project ManagementResearchCommunication

About the role

  • You will set the vision and execute scaling of Chai's lab-in-the-loop data platform, working hand-in-hand with our science and research teams.
  • You don't need a biology background, but you will need to pick it up while learning about our AI methods.

Responsibilities

  • Chai is a research lab working on generative models for molecular design.
  • Design & drive complex projects and workflows, across multi-party relationships.

Nice to have

  • Translate research goals into concrete deliverables-experiments, deliverables, success criteria, SOWs, timelines, trackers, etc.
  • Communicate highly technical information with clarity.
  • Juggle concurrent projects and priorities, adapting quickly and keeping everyone in sync.
  • Organized and scrappy
  • Detail-oriented and fast-moving

Skills

  • Someone who operates with urgency, has strong communication and project management skills, and who has an analytical nature.
  • Immerse yourself in technologies that represent the future of biotech, and position yourself at the forefront of this emerging field.

Compensation

  • We offer highly competitive compensation.

Benefits

  • Develop a vision for how we scale our data program by orders of magnitude.

Company info

  • We are building AI infrastructure to engineer new medicines with speed and precision.
  • Our mission is to unlock progress towards better cures and better science, and we see countless interesting problems on the road ahead.
  • We are known for talent density, rigorous research and pace of execution.
  • We are backed by Sequoia, Index, Thrive, General Catalyst, Dimension, OpenAI and others.
  • We are looking for a data operations expert, or a fast-rising generalist, to help drive data generation initiatives here at Chai.
  • We offer highly competitive compensation.

This listing is sourced directly from Chai Discovery's careers page and normalized into a canonical job model.