Product Management - Manager

PwC; Hyderabad, Chennai District, Bengaluru; Sur place; INR 900 000 - 1 500 000; Plein temps

Résumé du poste

PwC is seeking a senior product manager to own an AI-enabled product surface end-to-end—from discovery and roadmap to prioritization, launch, and iteration. You will defend the problem and quarterly plan, drawing on 6+ years in product management and experience shipping a zero-to-one product.

You will elicit needs from business and tech stakeholders, craft PRDs and acceptance criteria, and communicate with senior clients with honesty.

Qualifications

• 6+ years in product management, including a zero-to-one product.
• Writes clear PRDs and acceptance criteria, and manages senior stakeholders and clients with honesty and calibration.
• Hands-on with AI tools in their own workflow; used AI-assisted tooling to produce prototypes or tools.
• Practical understanding that AI outputs are probabilistic; can discuss accuracy, cost, latency, and when AI is not the right tool.
• Data-informed decision-making — defines metrics, runs experiments, reads funnels, ties decisions to business outcomes.

Connaissances

Product management

Stakeholder management

AI tooling

Data-driven decisions

Experimentation

Outils

AI tools

Description du poste

Role & responsibilities

Must-have

• Strong product fundamentals & product sense owns a product surface end-to-end: discovery, roadmap, prioritization, launch, iteration. Can defend why this problem, this quarter. 6+ years in product management, including a zero-to-one product.
• Requirements and stakeholder craft elicits needs from business and technical stakeholders, writes clear PRDs and acceptance criteria, and manages senior stakeholders and clients with honesty and calibration.
• Hands-on with AI tools in their own workflow has used AI-assisted tooling to produce working prototypes, wireframes, or internal tools themselves.
• Practical understanding of how AI products behave knows outputs are probabilistic, has thought about how to tell whether a model's answers are actually good, and can talk sensibly about accuracy, cost, latency, and where AI is the wrong tool.
• Data-informed decision-making — defines metrics before building, runs experiments, reads a funnel, and connects product decisions to business outcomes.

Good-to-have

• Design and UX instinct — comfortable producing high-fidelity interactive wireframes and reasoning about interaction patterns.
• Domain expertise— financial services, healthcare, energy, manufacturing, public sector.
• Depth in AI evaluation — has built or specified test sets and quality benchmarks for generative &agentic features.
• Technical background — engineering, data, or solution-architecture history; enough code fluency to read a PR and challenge an architecture.