Associate Director - Product

TMRW House of Brands; Bengaluru; On-site; INR 4,000,000 - 7,500,000; Full time

Job summary

TMRW House of Brands is seeking an Associate Director – Product to own consumer-facing product for one or more D2C brands plus platform capabilities used across multiple brands. You will oversee end-to-end storefronts from home to checkout, and manage AI-backed surfaces, analyzing metrics like conversion, AOV and repeat rate.

You will lead a small team, partner with brand, marketing and merchandising, and drive a rigorous experimentation program to ship high-impact features across web and mobile

Qualifications

• Must have 8–12 years of total experience with at least 5 in product management and 2+ directly managing product managers
• Proven ownership of a consumer-facing D2C, e-commerce or marketplace product at real scale with revenue accountability
• Hands-on experience with commerce fundamentals: catalog, search & discovery, cart & checkout, payments, promotions, order management and returns
• Strong analytical ability - SQL or equivalent, funnel analysis, cohort and retention analysis, A/B test design and interpretation
• Demonstrated experience building platform or horizontal capabilities used by multiple teams or brands
• AI judgment - shipped at least one AI or ML-backed feature and can explain evaluation, cost and fallback
• Excellent written and verbal communication; ability to write sharp specs and present to senior audiences
• Genuine craft sensibility - you can push design and engineering to a higher bar
• Systems thinking - reason across data flows, integrations and edge cases; multi-system workflow awareness
• Stakeholder management - capable with founders, business heads and functional leaders

Responsibilities

• Own end-to-end shopping experience for brand(s): home, category, search, PLP, PDP, cart, checkout, post-purchase and account
• Own AI-backed surfaces: ranking, sort, recommendations, semantic search and GenAI pipeline behind catalog content and product imagery
• Own conversion rate, AOV and repeat rate as primary metrics; set targets and drive the roadmap
• Run a continuous experimentation programme: hypothesis, test design, sample sizing, readout, rollout decision
• Partner with brand, marketing and merchandising on launches, drops, campaigns, pricing and offers
• Own the mobile app roadmap where relevant: onboarding, notifications, retention loops, app-vs-web parity
• Bring customer insights into decisions: qualitative research, session recordings, support tickets, reviews and NPS alongside quantitative data
• Own shared platform capabilities: multi-brand configurability, guardrails for model accuracy, latency and cost

Skills

Product management

Leadership

Analytics

SQL

A/B testing

Stakeholder management

AI judgment

Systems thinking

UX craft

Job description

We are a house of D2C consumer brands on a shared commerce platform - Bewakoof, Nobero, Wrogn, Urbano, Veirdo, NautiNati, TIGC and Juneberry. That creates a specific kind of product problem: every brand needs a storefront that feels distinct, while the plumbing underneath — catalog, checkout, payments, order management, returns, offers, personalisation — has to be built once and serve all of them.

The Associate Director – Product owns both halves. You will run the consumer-facing product for one or more brands (web and app, from first ad click to repeat purchase) and, in parallel, own a set of horizontal platform capabilities that every brand depends on. You are accountable for outcomes on your brand(s), how quickly you can build/scale horizontal capabilities, how fast you can onboard new brands on these platforms, and how much of the stack they can reuse. AI is part of both halves of that ownership: you decide where models belong in the product, where they don't, and how the ones that ship get proved out.

The defining challenge is the range. You’ll move between a consumer experiment measured in days, a multi-system operational workflow where the edge cases are the actual work, and a problem that needs data engineering and data science support before a line of UI gets built. Each demands a different mode of thinking and the same rigour.

Why this role

Most product roles give you one of the two: brand-level ownership with no leverage, or platform work with no line of sight to a customer. This one has both. You will see what you build show up in a customer’s cart this week, and see the same system carry the next brand we launch.

What you will own-

Brand product (D2C)

• Own the end-to-end shopping experience for your brand(s): home, category, search, PLP, PDP, cart, checkout, post-purchase and account.
• Own the AI-backed surfaces on your brand(s): ranking and sort, recommendations, semantic search, and the GenAI pipeline behind catalog content and product imagery. Define what good looks like, how it is measured, and when a simpler rule beats a model.
• Own conversion rate, AOV and repeat rate as primary metrics. Set the target, diagnose the funnel, and drive the roadmap that moves it.
• Run a continuous experimentation programme — hypothesis, test design, sample sizing, readout, rollout decision. Kill what doesn’t work quickly.
• Partner with brand, marketing and merchandising on launches, drops, campaigns, pricing and offer constructs, and make sure the product supports them without one-off hacks.
• Own the mobile app roadmap where relevant: onboarding, notifications, retention loops, and app-vs-web parity decisions.
• Bring the customer into the room. Use qualitative research, session recordings, support tickets, reviews and NPS alongside quantitative data — and hold the team to evidence over opinion.

Platform product (horizontal)

• Own a defined set of shared capabilities built across brands to drive profitable growth and the roadmap that makes each of them work for every brand.
• Own AI as a shared capability, not a per-brand experiment. Our ML and agentic services are multi-brand by design — one build, eight storefronts. You decide what gets built in-house versus bought, how models are evaluated before and after rollout, and where the guardrails sit: accuracy, latency, cost per call, and what the system does when the model is wrong.
• Design for multi-brand from the start: configurable, not forked. Make the trade-off between brand-specific flexibility and platform consistency explicit, and document where the line sits.
• Reduce time-to-launch for a new brand and time-to-ship for brand teams. Track and report both as platform metrics.
• Work with engineering leadership on architecture decisions, build-vs-buy calls and vendor selection for commerce infrastructure.
• Own the internal tooling side of the platform where it blocks operations — ops consoles, CX tooling, inventory and fulfilment workflows.

Leadership

• Translate business goals into a quarterly/annual roadmap with clear priorities, sequencing and stated non-goals — and defend it with data.
• Operate as the senior product voice with founders, brand heads and category leadership. Communicate crisply upward and give bad news early.
• Manage and grow a team of 3–4 Product Managers + PM Interns. Set the bar for writing, rigour and customer proximity; run 1:1s, reviews and hiring.
• Set the direction and run execution with rigour - decide what the team works on and why, discovery cadence, make sure solutions are thought through before they hit a sprint, spec quality, launch plans, experiment governance, prove impact after release, and grow a team of PMs into stronger operators.
• This is a builder’s role with a small team. You will still write specs, build working prototypes with AI tools, sit in on user calls, look at funnels yourself, and argue about the details of tech, business and analytics.

What we are looking for

Must have

• 8–12 years of total experience, with at least 5 in product management and 2+ directly managing product managers as their reporting manager, owning performance, growth and levelling. Managing interns, project managers or a dotted-line team does not meet this bar.
• Proven ownership of a consumer-facing D2C, e-commerce or marketplace product at real scale, with revenue accountability.
• Hands-on experience with commerce fundamentals: catalog, search and discovery, cart and checkout, payments, promotions, order management and returns.
• Strong analytical ability - SQL or equivalent, funnel analysis, cohort and retention analysis, A/B test design and interpretation. You pull and question the numbers yourself rather than just receiving them.
• Demonstrated experience building platform or horizontal capabilities used by multiple teams or brands, and navigating the configurability-vs-consistency trade-off.
• AI judgment - you have shipped at least one AI or ML-backed feature and can explain how you evaluated it, what it costs to run, and a case where you decided against a model. And you use AI tooling in your own work - prototyping, analysis, synthesis.
• Excellent written and verbal communication. Clear specs, sharp documents, and the ability to make a case to a senior audience.
• Genuine craft sensibility - you can tell good UX from bad and will push design and engineering to a higher bar.
• Systems thinking - you can reason across data flows, integrations and edge cases, and hold a multi-system workflow in your head.
• Stakeholder management - you can hold your own with founders, business heads and functional leaders.

Good to have

• Experience in fashion, apparel, beauty or lifestyle categories.
• Experience launching a brand or a new business line from zero to one.
• Familiarity with modern commerce stacks - headless storefronts, commerce engines, CDPs, martech and CRM tooling, recommendation and personalisation systems.
• Exposure to India-specific commerce realities: COD, RTO, tier-2/3 buyer behaviour, quick commerce, vernacular and low-bandwidth performance constraints.
• Experience partnering closely with supply chain and CX teams on post-purchase experience.
• Experience in operations, supply chain, marketing or internal platforms — you've built for users whose day job is running the business, and you understand why that's harder than consumer product, not easier.
• Depth in AI product — you have owned an ML-backed or agentic surface end to end, including data, evaluation, rollout and the fallback when it fails, or built LLM-based automation that held up at real volume.