Lead Product Owner

Job Description:

Role: Lead Product Owner

Overall Experience- 12+ Years

Experience as a Product Owner/Product Manager: 8+ Yrs

Mandate experience - Product Lifecycle, AI Environment, Product Strategy and Roadmap, Product Ownership (End-End)

Reports to: US Media, SVP of AI & Automation

Team: Product Owners (India-based); partners closely with Engineering Lead and Senior Software Engineers

Location: DGS – India (Shift- overlap hours with US Eastern required)

Role Overview:

Dentsu US Media is building a portfolio of AI-native automation products that differentiate dentsu's offerings, grow revenue, and deliver significant cost savings. The first product in the portfolio — iQA (intelligent Quality Assurance) — targets $13M+ in annual write-off liability generated by preventable campaign activation errors by validating programmatic campaign settings against trafficking instructions before launch. iQA is one example of the portfolio's ambition, not its boundary.

This DGS India-based role leads the product team that defines, ships, measures, and drives adoption of that portfolio, partnering closely with a dedicated Engineering Lead who owns technical execution. You own the product lifecycle from business case through retirement, manage Product Owners, coordinate DGS delivery resources, and are accountable for whether these products deliver measurable financial impact and durable differentiation — not just working software.

8–10+ years as a Product Owner or Product Manager on technical, data, automation, or AI-enabled products is expected. You should bring hands-on product leadership experience across most of the areas below, with the judgment to go deep where product risk or business value requires it. Candidates are not expected to match every tool, platform, or domain detail.

Define and sequence the AI automation portfolio roadmap across cost-saving products like iQA and revenue-differentiating products that strengthen dentsu's client offering.

Own the business case for each product or major experiment — problem sizing, investment rationale, value levers, go/no-go recommendation, and before/after measurement framework.

Design structured experiments with clear hypotheses, falsifiable success criteria, adjudication logic, phase-gate thresholds, and measured kill, pivot, or scale decisions.

Apply a phased methodology appropriate to product risk — dual-run validation, AI-primary with human review, and AI-autonomous workflows with approval gates where needed.

Identify expansion opportunities as products validate — new platforms, client segments, workflow steps, automation depth, or revenue-generating use cases.

Run structured discovery before PRDs are written — validate the problem, size the opportunity, confirm a product is the right answer, and reshape or stop low-value ideas early.

Write clear PRDs and acceptance criteria covering user workflows, edge cases, out-of-scope decisions, data quality, human review paths, and definition of done.

Prioritize the backlog and portfolio against value impact, stakeholder urgency, delivery capacity, experiment readiness, and product risk.

Maintain a healthy, value-backed delivery pipeline for DGS resources — keep upcoming work defined, prioritized, and ready for engineering, and surface when the portfolio does not have enough validated, high-value work to justify current team capacity.

Manage delivery dependencies outside engineering — vendor/API access, data governance approvals, Legal sign-off, stakeholder availability, and pilot readiness — before they stall delivery.

Set standards for direct-report Product Owners and assigned DGS delivery resources, developing product craft rather than only managing output.

A product nobody uses is not a product. Adoption is a product requirement, not a post-launch activity.

Define adoption strategy during product scoping — who needs to change behavior, what makes compliance easier than bypass, and how product output gets actioned.

Design workflow integration: where AI output surfaces, what happens when users disagree with the AI, how issues escalate, and how human review feeds product learning.

Measure product health after launch — coverage, action rate, override rate, time-to-resolution, defect patterns, and realized financial or competitive value — and feed those signals back into the roadmap.

Run enablement as a repeatable program and treat low adoption as a product defect to diagnose and improve.

Partner with the Engineering Lead on architecture tradeoffs: direct API versus reusable tool interfaces such as MCP, linear workflow versus orchestration, human-in-the-loop controls, latency, security, maintainability, and reusability.

Bring enough technical fluency with AI/LLM APIs, agentic workflows, data-backed applications, structured outputs, and production integrations to engage in tradeoffs and review outcomes, not direct implementation.

Hold delivery accountability for scope, timeline, and quality across active workstreams, while Engineering owns day-to-day technical execution and standards.

Review technical output against product requirements and identify when implementation misses requirements, edge cases, user workflow, data quality, AI review paths, or product risk controls.

Translate US business requirements from Channel Leads, Legal/Compliance, and Client Teams into DGS-executable plans without loss of intent or precision.

Reconcile conflicting requirements — for example, Activation speed versus Legal/Compliance caution — into clear product decisions and say no when the data supports it.

Coordinate early with Legal/Compliance on AI consent, client data governance, OSS usage, third-party tools/API connections, and pilot risk.

Communicate upward and sideways with precision: status, blockers, decisions needed, value delivered, pilot expectations, adoption risks, and phase-gate recommendations.

Experience shipping AI-enabled or automation-heavy products using Claude, OpenAI, Gemini, custom Python automation, or equivalent capabilities.

Familiarity with digital advertising workflows, trafficking instructions, bulk upload sheets, DSP campaign setup, IO structure, or programmatic validation/reconciliation.

Exposure to DV360, TTD, Meta Ads Manager, Amazon DSP, CM360, ad ops workflows, or comparable platform data/API environments.

Experience working with US and DGS stakeholders across time zones using structured async communication and clear escalation.

Experience launching revenue-generating products, not only cost-saving automation.

Not an engineering management role — product judgment is primary; technical fluency is important, but implementation ownership sits with Engineering.

Not a BAU operations role — you are building net-new products in an environment without a defined playbook.

Not a cost-savings-only role — the portfolio includes revenue-differentiating products, and you are expected to identify and pursue both.

Not a strategy-only role — you are accountable for shipped software, measured adoption, and realized financial impact.

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