At Secret Sauce, we work with some of the largest retailers and marketplaces in the world to transform how people shop online. We combine data from retailers with data we create using AI and machine learning to power innovative and powerful retail technologies. 

As a Senior Product Manager, you will have the opportunity to own the objectives and the roadmap of our fit & sizing related services. These services are integrated into some of the world’s top Fashion & Apparel e-commerce designers and brands such as Gucci, Walmart and are used by 100+ million people world-wide.

What you’ll be working on

  • Define the vision and future direction of our fit & sizing related services
  • Perform rigorous cost/benefit analysis and prioritize new feature development and maintenance tasks for engineering 
  • Orchestrate the execution of product development with cross-functional teams, such as Data Science/AI, ML Engineering, Backend, Frontend, Analytics and Data Engineering teams
  • Perform market analysis, stay up-to-date on market trends in both ecommerce and consumer-facing apps and services to drive our product roadmap 
  • Evangelize product strategy and direction to key company stakeholders and execution teams 
  • Define and continuously measure success against KPIs to maximize value for our retailer partners and end-users 
  • Proactively identify product weaknesses, risks, and improvement opportunities to maximize value for our retail partners 
  • Critically review processes and collaboration across teams to identify and push for opportunities that can yield product improvements



  • 3+ years experience in product management
  • B2B Saas experience 
  • Quantitative and qualitative data analysis experience
  • Ability to gather business requirements and specify features
  • Experience crafting and interpreting ab tests
  • Self-starter, ability to grasp the core context, lead cross-functional parties to achieve goals
  • Respectful and kind posture toward colleagues


Tech Stack

At Secret Sauce, we use the technologies and tools that we believe are right for the job at the time. We're not afraid to replace a technology or rewrite a service if gaining experience and understanding the domain better makes us realize that we made the wrong choice. We embrace change and work in a fast-paced environment which means that the technology stack we work with is what we believe is the best. That makes us quite happy.


Our backend system consists of independent services built using Java and Ruby that communicate asynchronously through Kafka. We use Avro and a Schema Registry to enforce these interfaces. All our services are packaged using Docker and deployed to our infrastructure in AWS using Kubernetes. Our infrastructure is immutable, we build AMIs with Packer and roll them out with Terraform. We don't have "DevOps" or an Ops team, we think of running services in a cloud environment as part of the software engineering role.

The services we provide to our retail partners are integrated into their existing websites; we provide a single JavaScript library that they can use to unlock all of our products. Analytics, AB testing, error reporting, real-user monitoring is built-in and is available to Fit Predictor, Style Finder, and our future services. The services themselves are built using ES6, React/Flux, and modern JavaScript tooling.

Our data team loves Spark and uses it to process large datasets that we receive from our partners and that we produce ourselves. We don't run a persistent cluster; we process and move data between different data stores: S3, Kafka, PostgreSQL, and Snowflake are all part of the equation and are used where they make the most sense. We rely on Databricks to manage our Spark clusters and use Apache Airflow to orchestrate tasks and to monitor, schedule, and retry jobs.

We started out as a small development team using Ruby and Rails. We ended up with our current architecture and tech stack not because we use technology for technology's sake, but because we believe they are the right choice with the right trade-offs for our expertise, needs, and size.