Strategic Customer Success Manager

 

  Prolific

Prolific is not just another player in the AI space — we are the architects of the human data infrastructure that is reshaping the landscape of AI development. In a world where foundational AI technologies are increasingly commoditized, it's the quality and diversity of human-generated data that truly differentiates products and models. Our Sales and Success team works with a broad range of enterprises and emerging AI labs to power their most critical data and model development initiatives.

 

The role

As an Enterprise Customer Success Manager, you will partner with a diverse book of enterprise customers — from large organizations building their own ML pipelines and AI agents to smaller AI labs scaling their research programs — ensuring they achieve meaningful, lasting value through our human data offerings. In this role, you will drive adoption, consumption, and expansion through a land-and-expand motion, acting as a trusted advisor across research, program management, and business stakeholders. You will own the end-to-end success lifecycle, guiding customers from onboarding to maturity, expansion, renewal, and advocacy while collaborating with your counterparts on Prolific's Services, Support, Sales, Solutions Engineering, Product, Marketing, and other teams.

 

What you’ll bring to the role

• 4+ years in a customer-facing enterprise customer success (and/or leadership) role, ideally in a business where value is aligned with increased consumption
• Experience developing success plans and coordinating cross-functional resources to drive positive outcomes with customers
• Operational rigor and experience managing a larger book of business — tracking health signals, forecasting consumption, and identifying expansion opportunities at scale
• Demonstrated success navigating mid-to-large organizations, driving adoption and expansion, and building strong stakeholder relationships
• Strong business acumen with the ability to interface with technical and business stakeholders (engineers, data scientists, ML leads, and program owners)
• Fundamental understanding of AI, machine learning, LLMs, or agent frameworks — enough to speak credibly about enterprise use cases and value levers
• A collaborative, growth-oriented mindset and desire to contribute ideas, best practices, and energy to an innovative, fast-moving team