Associate Product Manager

Wadhwani AI Global; New Delhi; Vor Ort; INR 1.800.000 - 3.400.000; Vollzeit

Zusammenfassung

Wadhwani AI Global (WAIG) is seeking a growth-oriented product/operations professional to own data collection, annotation quality, and app specs. You will define problem statements, collaborate with ML engineers, and lead field deployment across pilots.

The role grows into product ownership as projects expand. Initially you will manage end-to-end data workflows, coordinate field teams, and shape how we measure success.

Qualifikationen

• Must have at least 2+ years in product, programme, delivery, or operations with real users.
• Excellent written communication for asynchronous coordination across time zones.
• Ability to read API contracts and discuss model limitations with ML engineers.
• Comfort working with data, defining metrics, and identifying real signals.
• Willingness to travel for field work and deployment activities.
• Experience in global health, education, development, or field deployments.
• Experience running data collection or annotation operations with field teams or vendors.
• Exposure to grant funded delivery and donor reporting timelines.
• Familiarity with AI/ML products and data protection regimes (DPDP, GDPR, HIPAA).

Aufgaben

• Own data collection programmes day to day: field logistics, collector coordination, progress tracking.
• Run annotation and review loop; define good labelling and maintain quality metrics.
• Write specs, user flows, and acceptance criteria for mobile apps and internal tools.
• Own feedback loop after deployment: usage, drop-off, and failure modes.
• Be the contact for implementation partners and field teams on live pilots.
• Talk to users and partners to define constraints and write engineering scope.
• Support grant and partnership work with product framing and delivery narrative.
• Shape how the product prioritises and what we hold ourselves to.

Kenntnisse

Data collection management

Coordination across teams

Product lifecycle

Strong written communication

ML/AI fluency

Field travel readiness

Grants/donors reporting

Stakeholder management

Jobbeschreibung

About Job

Wadhwani AI Global (WAIG) is a next-generation AI transformation partner for governments and multilaterals across Africa, Latin America, and Asia. We combine cutting-edge AI products with deep advisory capabilities to unlock billion-dollar opportunities in health, education, and agriculture.

This is a role designed to grow. You will start with clearly defined ownership over data collection, annotation quality, and the specs behind our apps and internal tools. As the project portfolio expands, so does the scope, and we expect the person in this seat to be running products within a year. If you want to grow into product ownership rather than wait for it, this is the job.

Responsibilities

• Own our data collection programmes day to day: field logistics, collector coordination, progress tracking, and chasing down why the numbers look the way they do.




• Run the annotation and review loop. Define what good labelling looks like, work with annotators and domain experts, and keep quality measurable.




• Write the specs, user flows, and acceptance criteria for our mobile apps and internal tools from an agreed problem statement, then stay involved through build, testing, and release.




• Own the feedback loop after deployment: usage, drop off, failure modes in the field, and what changes as a result.




• Be the working contact for implementation partners and field teams on live pilots, covering scheduling, blockers, and what has to be true before a rollout starts.




• Talk to users and partners, work out what the actual constraint is, and write the scope that engineering and ML build from.




• Support grant and partnership work with product framing, scoping, and delivery narrative.




• Shape how the product works here: how we prioritise, how we decide, and what we hold ourselves to.

Skills & Qualifications

• 2+ years in a product, programme, delivery, or operations role, including something you shipped or ran that had real users.




• Strong written communication. Most coordination here is asynchronous and across time zones, and the written spec is usually the only spec.




• Enough technical fluency to work credibly with ML engineers: you can read an API contract, reason about what is expensive to build, and hold a conversation about model limitations without needing it translated.




• Comfort with data. You can define what to measure, work with a dataset, and tell the difference between a real signal and a small sample.




• Willingness to travel for field work and deployment. Understanding our users means spending time where they are.




• Experience in global health, education, development, or another field deployment context.




• Experience running data collection or annotation operations, including working with field teams or vendors.




• Exposure to grant funded delivery, including donor reporting and milestone driven timelines.




• Familiarity with AI or ML products.




• Exposure to data protection regimes (DPDP, GDPR, HIPAA), or to health or education data generally.