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Scale AI is a 2-sided marketplace. On the demand side we have customers that need labeled data to train their AI models or AI applications. On the supply side we have millions of contributors distributed worldwide. The biggest business problem we currently face is: identifying the right contributors from across the globe to complete tasks on a specific project. Identifying the right users enables us to deliver higher quality data to our customers sooner, with less waste. 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; Train contributors to understand what a good vs. bad task deliverable looks like.
Job Responsibility:
Understand our business, customer experience and ecosystem position
Set the product strategy for the contributor quality & enablement team including running several experiments in parallel
Develop and execute a data-driven, contributor-focused product roadmap through close collaboration with senior leadership, Operations, Data Science, Analytics, Design, and Engineering while balancing business needs
Translate customer and internal-user needs into clear, well-defined functional and technical requirements backed by data analysis and deep understanding of our users
Guide and interface closely with data analysis and engineering teams to define scope, review and refine technical capabilities, prioritize projects for release, and define new opportunities
Build long-term instrumentation, monitoring, and evaluation capabilities for product performance, tracking, and to create product insights
Establish business cases and projected return on investment to identify and prioritize opportunities
Partner with Finance and Business Leaders to manage the impact on the profitability of the overall business
Requirements:
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 especially as the % experts increases and datasets get more specialized and complex
Strong business acumen and analytical experience, with demonstrated success in defining emerging or ambiguous user behaviors and driving continuous iteration in high-uncertainty environments
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
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 (generally with several internal tools)
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