Founding Product Manager β€” AI-Driven Adoption

Founding Product Manager β€” AI-Driven Adoption

Full-Time 60000 - 80000 Β£ / year (est.) No working from home possible
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At a Glance

  • Tasks: Drive adoption of AI interface Atlas in the UK financial advice sector.
  • Company: AdvisoryAI, a leader in AI-driven solutions for financial services.
  • Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
  • Other info: Fast-paced environment with a focus on user feedback and rapid iteration.
  • Why this job: Be at the forefront of AI innovation and shape the future of financial advice.
  • Qualifications: Strong B2B SaaS experience and data-driven decision-making skills.

The predicted salary is between 60000 - 80000 Β£ per year.

AdvisoryAI is seeking a candidate to drive adoption of their AI interface, Atlas, in the UK financial advice sector. You'll define metrics for engagement and actively engage with users to improve product features based on direct feedback.

The ideal candidate will have a strong B2B SaaS background and be comfortable making data-driven decisions. A focus on user evidence and rapid iteration is essential in this fast-paced environment.

Founding Product Manager β€” AI-Driven Adoption employer: AdvisoryAI

AdvisoryAI is an exceptional employer that fosters a dynamic and innovative work culture, perfect for those passionate about driving AI adoption in the financial advice sector. With a strong emphasis on employee growth, we offer opportunities for professional development and encourage a collaborative environment where your insights directly shape our product's evolution. Located in the heart of the UK, we provide a unique chance to be at the forefront of technology while enjoying a supportive team atmosphere.

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Contact Details:

AdvisoryAI Recruitment Team

We think you need these skills to ace Founding Product Manager β€” AI-Driven Adoption

B2B SaaS Experience
Data-Driven Decision Making
User Engagement
Product Feature Improvement
Metric Definition
Rapid Iteration
User Evidence Focus