You will build products and systems to identify the right contributors to staff on each project, identify the right contributors to review tasks completed by other users, and train contributors to understand what a good vs. bad task deliverable looks like.
Scale operates a global marketplace for talent, working with Contributors in over 100 countries. Today, each project we have with a customer operates in a very bespoke style. We need to standardize data across all projects and centrally predict which contributors will succeed sooner. This will affect the experience all new and existing contributors will have.
This is a highly cross-functional role that will involve working with Dedicated Eng, design and data science staffing + PMs across the org, ML researchers to build models to predict which CBs will succeed, Operators who run our projects and have hands-on experience on what’s worked for them, and other enablement functions as we create course content.
You will understand our business, customer experience and ecosystem position, set the product strategy for the contributor quality & enablement team, develop and execute a data-driven, contributor-focused product roadmap, and guide and interface closely with data analysis and engineering teams to define scope, review and refine technical capabilities.
Ideally, you’d have 5-8 years of experience in Product Management in the tech industry, excellent communication and stakeholder management skills, capable of influencing across technical and non-technical audiences to drive strategic outcomes, treat contributors as valued customers, strong business acumen and analytical experience, and demonstrated success in defining emerging or ambiguous user behaviors and driving continuous iteration in high-uncertainty environments.
Nice-to-have: experienced in building scalable decision systems, partnering cross-functionally with policy, data science, and operations to enhance user protection and model reliability, experience working on EdTech or roles that require working closely with Ops, strong understanding of the AI market and willingness to stay at the cutting edge, deep understanding of modelling workflows, including data labeling, model training, inference, and deployment, with the ability to translate technical concepts into actionable product strategies, experience building products from the ground up, seeing them through the scaling journey of a business, and dogfooding to understand the users’ perspective.
Bachelor’s or advanced degree in a quantitative, engineering, or related discipline, with strong comfort in engaging deeply with technical and data-driven problem spaces.
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