Senior Product Manager Top Fintech company only at Staffnixcom · Gurugram · 6 - 10 years · ₹47L - ₹52L / yr · Bootstrapped · Posted 10 Aug 2026

Strong Senior Product Manager — Vertical Products (Fintech, Growth & Conversion) Profile
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Mandatory(Experience 1)- Must have 6+ years of product management experience, with the most recent 3 years spent owning a consumer-facing product surface (app, web funnel, onboarding, checkout, or equivalent B2C surface).
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Mandatory(Experience 2) - Must have consumer-facing product ownership as the centre of gravity. Backend, API, infra, or internal-tools PMs with no recent consumer-facing surface will not be considered. Backend exposure alongside real consumer-funnel work is fine; a backend centre not.
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Mandatory(Experience 3)- Must have a metric-backed track record improving conversion, revenue yield, engagement, or lead quality in a consumer funnel — owned numbers, a steady experimentation cadence (A/B tests, rollouts, readouts), and comfort with analytics: funnels, cohorts, drivers, causal reasoning, measurement hygiene.
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Mandatory(Experience 4)- Must be fluent with AI — active, habitual use of AI tools (ChatGPT, Claude, Gemini, Perplexity, or equivalent) in daily product practice.
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Mandatory(Stakeholder Mgmt)- Must be able to act as the primary link between business leaders, tech teams, and external partners — communicating roadmap, priorities, trade-offs, and performance readouts to senior stakeholders, and working with distributed teams across time zones (Australia, India, Philippines).
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Mandatory(Product Writing & Agile Delivery)- Must have strong product writing — problem briefs, PRD-lite/user stories, experiment plans, decision logs — and deep working command of Agile frameworks and tools.
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Mandatory(Company) - Must be from B2C product company. FinTech is the priority . A non-fintech candidate qualifies only if currently or recently at a Series B plus company.
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Preferred(Skill)- Exposure to LangChain, RAG, or prompt engineering in a product context; experience working directly with data science/ML teams on model-backed features; prior AI features in transactional or financial-services consumer UX.

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