Lead Product Manager

Job description

Dr. Reddy’s Laboratories Ltd. is a leading multinational pharmaceutical company based across global locations. Each of our 24,000 plus employees comes to work every day for one collective purpose: to accelerate access to affordable and innovative medicines because Good Health Can’t Wait. We started in 1984 with a modest investment, 20 employees and a bold vision. Today, we have research and development centres, manufacturing facilities or a commercial presence in 66 countries. For nearly four decades, we have stood for access, affordability and innovation based on the bedrock of deep science, progressive people practices and robust corporate governance. As the pharmaceutical industry evolves and undergoes disruption, we see an opportunity – to strengthen our core further (the next steps) and to build the future (the new bets). ‘The Next and the New’ is how we aim to continue to be the partner of choice – purpose-driven, future-ready and sustainable. Our aim is to reach over 1.5 Bn+ patients across the world by 2030 by growing our core businesses and building for the future with sustainability at the core of our purpose and strategy. Sustainability for us means operating in a manner that respects people, planet and purpose – helping us conserve precious resources, serve our patients, create value for stakeholders, give back to society, fulfil our potential and maintain our integrity and transparency Dr Reddy’s maintains a work environment, free from discrimination and is an equal opportunity employer. We are committed to employ & nurture all qualified diverse workforce without regard to race, colour religion, nationality, sex, age, disability status, genetics, sexual orientation, gender expression, citizenship or any other characteristic or classification protected by applicable law(s) of the country we operate in. We treasure every talent, and recognize merit and diversity in our organization. " 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 v