Lead Product Manager

Role Overview

Dr. Reddy's Europe Generics and Consumer Health businesses have moved past AI experimentation. Copilot is deployed at scale, an enterprise-wide enablement programme is running, and a pipeline of AI agents is in build across marketing, commercial, supply chain, quality and finance. What the business now needs is a single, named owner who turns that momentum into products that create measurable value — and who is the one person a market head, brand lead or functional owner can call when they have an AI question or an idea.

The Lead Product Manager – AI is that person. This is a business-facing product role, not an engineering role. You will own the AI agenda for Europe Generics and Consumer Health end to end: finding the use cases worth doing, building the value case, shaping the product with the business, and driving adoption until the benefit shows up in the P&L; or in a process KPI. Technical ownership of AI platforms, models and engineering delivery sits with the central Digital / AI Centre of Excellence (CoE); you are their business counterpart, not their substitute.

Two mandates define the role:

• Single point of contact for AI for Europe Generics & Consumer Health — the front door for every AI question, idea and use case, and the owner of the overall AI adoption agenda for the region.
• Owner of AI-enabled products that generate value — turning business problems into AI products that are used, trusted and measurable.
Key Responsibilities

1. Own the AI agenda for Europe Generics & Consumer Health

• Act as the single point of contact for AI across both businesses — for market teams, functional leaders and the CoE alike.
• Maintain one prioritised view of the regional AI portfolio: what is live, what is in build, what is parked and why.
• Run the intake process for AI ideas — triage, qualify, decide build / buy / defer / decline, and give the requester a clear answer quickly.
• Represent Europe in enterprise AI forums and bring enterprise capability back into the region rather than rebuilding it locally.
2. Drive AI adoption and capability

• Own adoption as a measurable outcome — active users, frequency, depth of use and workflows genuinely redesigned around AI, not licences issued.
• Run the enablement rhythm: training, live use-case demonstrations, hands-on workshops, 1:1 coaching and open AMA forums; sustain and scale what already works.
• Build a network of AI champions across markets and functions so capability compounds locally instead of depending on one person.
• Identify where people are working around AI rather than with it, and fix the cause — trust, usability, training or process design.
3. Build AI products that create value

• Work with business owners to find problems where AI creates a real advantage, and say no where it only adds novelty.
• Build the value case: baseline, expected benefit, cost to serve (including inference and licence cost), and the KPI the product will be judged on.
• Own the product definition — user journeys, workflow design, requirements, acceptance criteria, human-in-the-loop and fallback paths, and the roadmap.
• Define what 'good enough to ship' means with the business: quality bars, evaluation criteria, confidence thresholds and escalation to a human.
• Partner with the CoE through discovery, build, pilot and scale — you own the what and the why; the CoE owns the how.
• Own launch readiness and post-launch: monitor output quality and usage, and decide what to improve, scale or stop.
4. Governance, compliance and trust

• Ensure every AI product respects data privacy, information security, legal / medical / regulatory review and, where applicable, GxP validation requirements — working with the CoE, Quality, Legal, InfoSec and Privacy teams.
• Make responsible-AI considerations — accuracy, bias, transparency, human oversight, auditability — part of the product definition, not an afterthought.
• Keep a defensible record of benefits claimed and benefits realised.
 

Required Qualifications & Skills

• MBA or equivalent postgraduate qualification preferred; strong analytical or technical undergraduate background is an advantage.
• 8+ years in product management, digital transformation or business-technology roles, with at least 2 years owning AI, data or analytics products that reached production.
• Working fluency in how AI products are actually built and run — LLMs, agents, retrieval over enterprise data, evaluation, human-in-the-loop design, model cost and latency trade-offs. Enough depth to challenge a solution design and to be credible with engineers; no requirement to build or train models.
• Demonstrated ability to translate a business problem into a product, and a product into a measured business outcome.
• Strong commercial literacy — able to build and defend a value case and to speak the language of marketing, commercial, supply chain or finance stakeholders.
• Proven change management and adoption track record: can evidence a team or process that was genuinely rebuilt around a new way of working.
• Excellent communication and stakeholder management skills, including with senior leadership and multi-country European teams.
• Experience in a regulated industry (pharma, healthcare, financial services) and familiarity with data privacy and validation requirements is an advantage.
Key Competencies

• Business-first product thinking — value before technology
• Judgement on where AI belongs, and the confidence to say where it does not
• Influence without authority across markets and functions
• Change and adoption leadership
• Data-driven decision making and benefit realisation
• Ownership, speed and rigour in execution
Measures of Success (first 12 months)

• A single, credible AI portfolio and intake process in place for Europe Generics & Consumer Health, with a published response standard for new requests.
• Agreed adoption targets met, measured on active usage and depth of use — not licences deployed.
• A defined number of AI products moved from pilot to production with a named business owner and a tracked KPI each.
• Benefit realisation reported against value cases, with variances explained honestly.
• The business treats this role as the default first call on AI.

Function: Other
Experience Level: Mid-Senior Level