Staff Data Science Tech Lead (training)

WHOOP is an advanced health and fitness wearable, on a mission to unlock human performance. WHOOP empowers its members to improve their health and perform at a higher level by providing a deep understanding of their bodies and daily lives.

As a Staff Data Science Tech Lead on our Training team, you will develop algorithmic features and metrics that capture aspects of daily movement, exercise, and training. Leveraging various data sources to combine WHOOP data with “gold standard” truth data, as well as drawing upon clinical theory and evidence, you will work with a team of data scientists to design, train, deploy, and maintain machine learning algorithms to provide insights to members on a wide range of topics. As part of our production data science team, you'll work with MLOps engineers to create and maintain robust services that host data science algorithms. You'll partner with the product team to identify new opportunities where we can leverage our large datasets to offer novel insights to our members. This role will serve as a technical leader for the Data Science Training team but will not directly manage other individual contributors.

RESPONSIBILITIES:

• • In this role you will be responsible for designing, developing, and implementing WHOOP algorithms using large-scale machine learning models. These models use data collected via the WHOOP Strap and mobile application to capture unique aspects of an individual's behavior and physiology, helping members achieve their health and fitness goals.
  • Work closely with a team of data scientists in developing algorithms that power member-facing features, managing a portfolio of projects in various stages of development at any given time
  • Conduct literature reviews and research projects to power evidence-based features for Members, continuously improving the teams' knowledge of physiological and data science methods
  • Collaborate with WHOOP Labs and our in-house clinical research team to design novel studies to collect “ground truth” data necessary for training machine learning algorithms
  • Create, improve, and maintain production services that provide analysis for movement, exercise, and training in collaboration with MLOps Engineers
  • Work with Data Engineers to improve data pipelining, tooling for machine learning, and systems for quality and validation
  • Mentor other data scientists on the team, regularly providing actionable feedback for more junior data scientists on both technical and non-technical aspects
  • Periodically serve as the on-call data scientist to respond in real time to incidents affecting production services

QUALIFICATIONS:

• • Bachelor's Degree in Mathematics, Statistics, Computer Science, or a related field
  • 7+ years of full-time professional experience in a related area
  • 7+ years experience applying advanced mathematical and statistical techniques
  • Significant experience working with time series data, preferably with wearable data applications
  • Proficiency in scientific Python and SQL
  • Experience deploying services and maintaining live code through logging and monitoring within a production environment
  • Excellent verbal and written communication skills

This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.

Interested in the role, but don't meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.

WHOOP is an Equal Opportunity Employer and participates in E-verify to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.