Who we are

WeightWatchers is inherently a data product and we can leverage our data—a massive longitudinal dataset across a wealth of touch points and millions of members in different geographies and demographics—to create a profoundly personalized and impactful experience. The Data Science team is a separate entity from the analytics, data warehousing and data engineering teams, freeing us up for a singular focus: building awesome data products.

What you will do

The WW Data Science team is looking for a new senior member to help us build the infrastructure that powers our predictive models, recommenders, and data enrichment algorithms. In your role as a ML Ops Engineer you will help WW’s Data Science team to develop, deploy, monitor, and maintain production machine learning (ML) models. You will build out and monitor CI/CD pipelines for automated image builds, running of tests, and deployment of models using e.g. GitHub Actions.  You will be evaluating existing ML processes to identify areas of improvement, and will apply the latest open source technologies in the ML stack to enhance WW’s predictive automation software.

Who you are

Along with production level software experience, you should demonstrate solid data science knowledge and understanding.

• Python is your jam! Along with python, you are strong in SQL.

• Machine learning product development experience, using state-of-the-art tooling

• Ability to create abstractions, APIs, and libraries

• Extensive knowledge of ML frameworks/libraries (e.g. scikit-learn, Pytorch, Tensorflow, transformers), data structures, data modeling, and software architecture within a cloud production environment

• Experience with Docker and Kubernetes

Base salary may vary depending on, but not limited to: skills, experience, and location. 

Base Salary

$110,000/yr to $185,000/yr