About the Role:

As Product Manager, Growth & Strategy, you will own end-to-end experimentation across the customer funnel: acquisition and conversion, activation and cross-sell, retention and repeat behavior. You'll work with real subscription cohort data at scale, translate messy behavioral signals into sharp hypotheses, and ship fast using modern AI tooling to compress the time between idea and shipped experiment.

This is a high-ownership, low-hand-holding role. You'll partner directly with engineering, design, analytics, and marketing, and you'll regularly present strategy, trade-offs, and experiment outcomes to senior leadership.

What You'll Do:

- Hypothesis-Driven Discovery: Mine funnel data, qualitative feedback, and competitive research to identify growth opportunities. Convert vague problems into sharp, testable hypotheses tied to specific user behaviors or metrics.

- Experiment Design: Define the right success metrics (conversion, retention, ARPU, repeat rate, cross-sell CVR, etc.). Design rigorous experiments with proper control/treatment splits, adequate sample sizes, and clean measurement - no vanity-metric experiments.

- Rapid Prototyping & Build Velocity: Use GenAI tools and low-code/no-code builders to go from idea to clickable prototype to live experiment fast - without waiting on a full engineering cycle for every test. You should be comfortable mocking up flows, writing spec-quality PRDs, and even shipping lightweight MVPs solo before looping in the full pod.

- Cross-Functional Execution: Run experiments end-to-end with engineering, design, analytics, and marketing. Track weekly progress, unblock the pod, and adjust scope or design mid-flight based on early signal.

- Analysis & Learning: Analyze results using statistical rigor - not just "did the number go up." Understand why an experiment won or lost, and turn every outcome (including failures) into a reusable product insight.

- Scale What Works: Take winning experiments from pilot to full rollout fast, and look for adjacent opportunities to extend the win (new segments, new surfaces, new markets).

- Stakeholder & Executive Communication: Communicate what's being tested, what's been learned, and what's next - clearly and concisely - to cross-functional stakeholders and the CEO's office. Build the business case to unlock further resourcing for what works.

- Build a Culture of Experimentation: Champion testing discipline, data-driven decision-making, and comfort with iterative failure across the broader product org.

Key Responsibilities:

- Analyze product funnels and user journeys (acquisition, conversion, activation, cross-sell, retention) and recommend optimizations against target metrics.

- Own execution of aligned growth initiatives from PRD through shipped experiment to scaled rollout.

- Design and run A/B tests across pricing and cashback structures, membership/VIP renewal mechanics, cross-sell surfaces, onboarding, and delivery experience.

- Use GenAI tools for research synthesis, prototyping, content and copy generation, data exploration, and rapid MVP building as a default part of the workflow, not an afterthought.

- Rapidly prototype and validate end-to-end product journeys, from a rough idea to a testable flow, without always waiting for a full engineering build.

- Continuously benchmark Country Delight against quick-commerce and D2C competitors to identify gaps and opportunities in pricing, UX, features, and lifecycle communication.

- Partner with data and analytics to ensure every funnel stage is properly instrumented and measurable, including diagnosing when a metric's movement (e.g. a rate improving while absolute volumes don't) is telling a misleading story.

- Translate qualitative and quantitative insight into a prioritized, high-conviction experimentation roadmap.

Requirements:

- 2-5 years of product management or growth experience (not more than 5 years); prior experience with a D2C, subscription, or membership-based consumer business is strongly preferred.

- Strong problem-solving and analytical skills, comfortable with funnel analysis, cohort analysis, and experiment design and statistics.

- Data-driven and inquisitive by default; you form a point of view from evidence, not gut feel.

- Hands-on fluency with GenAI tools for research, prototyping, content generation, and analysis. This isn't optional, it's core to how this pod works.

- Ability to rapidly prototype: wireframing, clickable prototypes, and low-code/no-code builders to test ideas before committing engineering time.

- Working familiarity with product analytics and experimentation tooling (any of the standard event analytics, dashboarding, or A/B testing platforms).

- Self-starter with a strong bias toward MVP and shipped learning over polished perfection.

- Excellent stakeholder communication, able to flex between a data scientist, an engineer, and a leadership conversation in the same day.

- Post-graduation preferred (MBA or equivalent).