Product Manager @Jaggaer, Hyderabad

Experience: 3+ Years

Location : Hyderabad

Work Mode: Hybrid (3 Days office & 2 Days WFH)

Notice Period: Immediate to 30 Days

Skills Required:

• 3+ years of product management experience in SaaS or enterprise software, including ownership of a product area end to end.
• Hands-on experience with AI products, including LLM-powered applications, prompt engineering, retrieval architectures, or applying AI to structured enterprise data.
• Strong working understanding of data platforms, semantic and dimensional modelling, and analytics or BI tooling. Snowflake experience is an advantage.
• A rigorous approach to accuracy: comfort defining ground truth, designing evaluation sets, and holding a quality bar under delivery pressure.
• Experience working with enterprise customers to validate problems and quantify value, rather than gathering feature requests.
• Experience with procurement, source-to-pay, supply chain or finance data is an advantage.
• Proficiency in Agile methodologies and working closely with distributed engineering teams.
• Bachelor's degree in Computer Science, Information Systems, Engineering, Business, or a related field preferred.

Role Responsibilities

• Product ownership: Own the strategy, roadmap and delivery for the analytics and business intelligence suite, Bulk Data Export, and the data products built on them.
• AI-native insight capabilities: Define and deliver new AI capabilities in this space, including generated insight and anomaly detection across customer spend, taking them from concept through early access to general availability.
• Product direction: Define what analytics and data products become as AI absorbs them, including how customers consume data when their own systems and agents are the consumer rather than a person, and where the boundary sits between surfacing an insight and acting on it. Establish the product direction for agent-ready data delivery.
• Data trust and accuracy: Own the quality bar for the data and semantic models our AI reasons over: the definitions of core procurement measures, the evaluation approach that proves accuracy, and the standard a model must meet before it is used with a customer. Committed delivery: Meet the existing analytics, reporting and data export commitments already sold to customers, including those carried in active RFPs and renewals, and keep the install base on a clear roadmap.
• Cross-functional collaboration: Work closely with engineering, applied AI, UX, and the Experience, AgentOS and Deep Research product managers, so the data and intelligence layer serves the whole JAI portfolio rather than one surface.
• Market and customer insight: Validate problems and proposed capabilities directly with customers and prospects, and use that evidence to prioritise. Support analytics and data content in RFPs and executive briefings.
• Agile execution: Translate product requirements into clear, well-defined requirements and acceptance criteria for the engineering teams, and work with them through delivery