New opportunity

Senior Product Manager - Retail Planning

Lyric · Bengaluru, India

About this role

Lyric is an enterprise AI platform built for supply chains, and we are seeking a Senior Product Manager to lead our Retail Planning capabilities. You will move beyond digitizing existing workflows to build agent-first product experiences that reason, recommend, and execute planning tasks autonomously. Responsibilities: Own product strategy and execution for retail planning workflows across Hardlines and Softlines. Translate complex planning problems into clear product primitives. Collaborate with AI/ML and engineering teams to design human-agent interactions. Conduct deep customer discovery with retail planners and leaders to identify opportunities for autonomous decision-making. Define success metrics and drive products from discovery through adoption. Requirements: 6+ years of Product Management experience, ideally in complex B2B, enterprise, data, or optimization products. Strong understanding of retail planning domains such as assortment, inventory, allocation, replenishment, and pricing. Ability to reduce complex business systems into clear product abstractions. Experience collaborating with data science and AI/ML teams. Excellent communication and customer discovery skills. Preferred: Experience working within a major retailer or with enterprise retail customers. Experience building AI-native or agentic products. Success in this role involves becoming an internal expert on retail planning, building trusted customer relationships, and shipping products that replace manual planning work with autonomous AI agent collaboration.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Product ManagementRetail PlanningB2B Enterprise SoftwareData-heavy ProductsAI AgentsSupply Chain OptimizationCustomer DiscoveryProduct StrategyRoadmap Prioritization

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Designing Autonomous Human-Agent Workflows
The core mandate is to move beyond digitizing existing manual tasks. You must define which retail planning decisions (assortment, inventory, replenishment) can be fully automated versus those requiring human oversight, and design the interaction model for this collaboration.
Translating Domain Complexity into Product Primitives
You must distill messy, interconnected retail planning problems into clear, scalable product abstractions. This requires deep expertise in Hardlines and Softlines retail to build solutions that generalize across different enterprise operating models.
Driving Adoption Through Outcome-Based Product Strategy
Success is measured by the actual replacement of manual planning work rather than feature delivery. You must define success metrics that track changes in planner behavior and ensure the product delivers measurable improvements in decision-making.
Deep Customer Discovery and Partnership
You are expected to spend significant time with retail planners and leaders to separate surface-level requests from underlying business problems, ensuring the product roadmap addresses the root causes of planning inefficiencies.

Likely challenges to think through

Balancing Autonomy with User Trust
A significant challenge is the 'black box' nature of AI. You must balance the sophistication of autonomous agents with the need for explainability and control, ensuring planners trust the system enough to relinquish manual tasks.
Navigating High Ambiguity in Agentic Product Development
As you are building agent-first products rather than standard software, there are few established patterns. You will need to iterate rapidly in an environment where the definition of 'success' for an AI agent is still evolving.
Managing Cross-Functional Technical Alignment
You must effectively bridge the gap between complex retail domain requirements and the technical constraints of AI/ML engineering teams, ensuring the product is both business-relevant and technically feasible.

Practice your answers

Technical practice

Defining Product Primitives in Retail Planning

What to coverExplain your process for decomposing complex, interconnected retail planning problems into clear product primitives. Focus on how you identify the core data entities and logic required to build scalable, reusable software components.

Human-Agent Interaction Design

What to coverDescribe how you design interfaces for AI agents that provide leverage to planners without stripping away necessary human judgment. Discuss the trade-offs between full automation and human-in-the-loop systems in high-stakes retail environments.

Autonomous vs. Assisted Decision-Making

What to coverHow do you determine which retail planning decisions should be fully automated versus AI-assisted? Provide a framework for evaluating the risk, complexity, and business impact of delegating specific tasks to an AI agent.

Data-Heavy Product Strategy

What to coverDiscuss your experience managing products that rely on large-scale data sets for optimization. Address how you collaborate with data science and engineering teams to ensure the product remains performant and explainable to end users.

Generalizing Customer Requirements

What to coverRetailers often have unique operating models. Explain your approach to identifying the underlying problem behind specific customer feature requests to build a product that generalizes across different retail categories and channels.

Measuring Success for Autonomous Products

What to coverBeyond standard feature delivery metrics, how do you measure the success of an AI-native product? Suggest specific KPIs that track changes in planner behavior and the actual improvement in retail decision quality.

Balancing Autonomy and Explainability

What to coverWhen building autonomous planning agents, how do you balance the need for high-speed execution with the requirement for explainability? Discuss how you ensure planners trust the recommendations provided by an AI system.

Retail Domain Expertise Application

What to coverHow would you apply your knowledge of assortment, inventory, or replenishment to identify a 'step-change' improvement in current planning workflows? Focus on moving beyond digitizing existing manual processes to creating new value.

Behavioural practice

Navigating Ambiguity in Product Strategy

What to coverDescribe a situation where you had to define a product strategy in a highly ambiguous environment. How did you align stakeholders and prioritize the roadmap when the path forward was not clearly defined?

Deep Customer Discovery

What to coverProvide an example of a time you conducted customer discovery that went beyond surface-level requests. How did you uncover the root cause of a planner's pain point and translate that into a successful product feature?

Collaborating with Technical Teams

What to coverDescribe a challenging project where you worked closely with AI/ML or engineering teams. How did you bridge the gap between business requirements and technical feasibility to deliver a high-quality product?

Driving Adoption of New Workflows

What to coverTell me about a time you introduced a new, non-traditional workflow to users who were accustomed to manual processes. How did you manage resistance and drive adoption of the new, more automated approach?

Sharpen your knowledge

Topics to prepare

Agent-First Product Design
Review the conceptual shift from traditional UI-driven workflows to agentic systems that reason, recommend, and execute tasks. Exercise: Map a standard retail replenishment workflow and identify which steps can be fully automated by an AI agent versus those requiring human-in-the-loop oversight.
Retail Planning Primitives
Deepen your understanding of core retail planning domains including assortment, inventory allocation, and pricing. Exercise: Deconstruct a complex retail planning problem into its fundamental data inputs and decision logic to demonstrate your ability to create clear product abstractions.

Skills to revise

Customer Discovery for Enterprise AI
Practice techniques for uncovering the root cause of planning inefficiencies beyond surface-level feature requests. Exercise: Prepare a set of interview questions for a merchandise planner that focuses on identifying their current manual bottlenecks and the specific context required for an AI to assist them effectively.
Cross-functional Collaboration with AI/ML Teams
Develop the ability to translate business requirements into technical specifications for data science and engineering teams. Exercise: Draft a product requirements document (PRD) for an autonomous inventory allocation feature, clearly defining the success metrics and the expected human-agent interaction model.

Your preparation checklist

Map Retail Planning Workflows to AI Opportunities
Create a document identifying three specific retail planning tasks (e.g., assortment, replenishment, or allocation) where manual effort is high. For each, outline a 'before' state and an 'after' state where an AI agent handles the execution, demonstrating your ability to move beyond simple digitization to autonomous workflows.
Prepare a Case Study on Human-Agent Interaction
Develop a brief presentation or write-up detailing a past project where you designed a system requiring human-in-the-loop decision-making. Explicitly explain how you balanced user control, trust, and explainability with the need for AI-driven automation.
Draft Strategic Questions for Stakeholders
Prepare a list of insightful questions for the interviewers focusing on how Lyric handles the 'cold start' problem for new retail clients, how they measure the success of autonomous agents versus human planners, and how they prioritize between Hardlines and Softlines product requirements.
Refine Your Product Abstraction Framework
Prepare to discuss your methodology for reducing complex retail business systems into clear product primitives. Be ready to explain how you translate messy, interconnected planning problems into actionable requirements for engineering and data science teams.

Availability last checked: 19 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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