Jay Jhaveri is a seasoned product leader with over 15 years of experience in conceptualizing, building, and scaling innovative technology products. He has a strong background in SaaS, enterprise software, and data-driven product development, with a focus on creating exceptional user experiences and driving business growth. Jay is passionate about leveraging technology to solve complex problems and is known for his strategic thinking, customer-centric approach, and ability to lead high-performing cross-functional teams.
Jay Jhaveri's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Spearheaded the product vision, strategy, and roadmap for a new B2B SaaS offering, resulting in successful market entry and exceeding first-year revenue targets by 20%.
Drove the product development lifecycle for a flagship mobile application, growing the active user base from 100K to over 1 million MAU through iterative improvements and feature innovation.
Championed and delivered an AI-driven personalization engine that increased user engagement by 30% and conversion rates by 15% across multiple product lines.
Established and scaled a product management team from 2 to 10 members, fostering a culture of innovation, ownership, and continuous learning.
The Institute of Chartered Accountants of India - Year 1995
Mulund College of Commerce - Year 1987
Highperformr Signals uncover buying intent and give you clear insights to target the right people at the right time — helping your sales, marketing, and GTM teams close more deals, faster.
Omni Interactions is a business process outsourcer (BPO) that provides on-demand, virtual customer service solutions. They utilize a gig-economy model, connecting businesses with a network of remote brand ambassadors (customer service agents) to handle customer interactions. This approach offers scalability and flexibility for companies needing to manage fluctuating call volumes and diverse customer support needs, leveraging AI to optimize agent matching and performance.
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