Data & AI Product Manager - India

• You have taken a data or AI product to market -- not internal tooling, not a feature within a larger product, but a discrete product that external customers evaluated, negotiated, and paid for.

• You have designed a commercial model for a product where value is variable and outcome-linked. You understand the difference between value-based pricing and cost-plus pricing and can explain when each is appropriate.

• You have run commercial discovery conversations at VP or C-suite level -- not just with product users or technical stakeholders. You understand how a CFO evaluates a data product differently from how an ops director does.

• You can build a financial case from operational data. The commercial conversation for this product requires translating system performance metrics into an operator P&L impact that a CFO will accept.

• EV, energy, smart grid, infrastructure, or industrial IoT sector product experience

• SLA-based or outcome-linked commercial model experience: contracts where payment depends on measured performance

• Cross-geography commercial experience: operator conversations spanning different regulatory environments and commercial cultures

• Technical credibility: able to explain what the AI system does without misrepresenting it -- CPOs ask hard questions and will not sign contracts with someone who does not understand the product

• Proof period design: has structured a time-limited pilot that produced a clear, data-backed commercial decision at the end

• You have only managed internal product roadmaps or feature backlogs. This role requires commercial ownership -- pricing decisions, contract terms, and the revenue that comes from signed contracts.

• You have not had commercial conversations with CFO or COO-level buyers. The product conversation for AI systems that affect operational costs and revenues happens at a different level than typical product discovery.

• You cannot explain the difference between a managed fee and a performance-based commercial model, or when each is the right choice for an operator in a specific situation.