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Open nowFirst seen 24 hours ago

Data & AI Product Manager - India

driivz/c4.00b8 open roles

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Your applicationOpen nowData & AI Product Manager - Indiadriivz/c4.00b

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. driivz/c4.00b postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: first seen 24 hours ago

driivz/c4.00b median: 1 days open

The posting

The role

This role exists at the intersection of a technically complex AI system and a commercial market that is still learning what it will pay for. As Data and AI Product Manager you will define what Driivz's data and AI capabilities look like as products that operators buy -- the packaging, the pricing logic, the proof-of-value framework, and the commercial conversation that converts a pilot into a contract.

You are not a scrum master and you are not a backlog prioritiser. You are the person who understands what operators will pay for before the team builds it, designs the commercial model that makes the product a business, and manages the pilot operators whose experience generates the proof points that make the next sale possible. You work from India but you spend significant time in commercial conversations with operators across multiple geographies.

What you will own

Product and commercial

• AI product scope and packaging: what gets built into a discrete product that operators pay for, in what tiers, at what price points

• Commercial model design: the pricing logic that connects the value Driivz generates for operators to a fee structure that is fair, credible, and scalable

• Proof-of-value framework: the structure of pilot periods including baseline measurement, tracked metrics, and conversion logic -- the 90-day pilot must make the commercial decision obvious, not require a separate sales effort

• Guardrail framework: translating operator preferences for AI decision boundaries into documented product configuration requirements for the engineering team

Customer and market

• Pilot operator management: running the proof periods of the first 3 to 5 operator pilots from connection to contract

• Operator discovery: 5 or more structured discovery conversations per quarter with operator ops directors and commercial leadership -- understanding what they will pay for before building it

• GTM with CS and commercial: translating AI system capabilities into the language and framing that closes a conversation with a CFO or COO

• External operator pipeline: identifying and engaging operators outside Driivz's existing customer base who are candidates for standalone AI product adoption

What we are looking for

Non-negotiable

• 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.

Strong signal

• 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

This role is not right for you if

• 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.

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