Senior Product Manager (Consumer Growth)

Hiring for a Client -CIMET

Key responsibilities

1) Product vision & strategy

• Define and articulate the vertical product vision aligned to CIMET's business goals and growth roadmap.

• Own the product lifecycle from ideation and research to launch and post-release optimisation.

• Translate strategy into a focused quarterly bets plan with clear targets.

• Identify opportunities to enhance engagement, conversion, lead quality, and retention — including

through AI-driven personalisation, intelligent pre-filling, and LLM-powered recommendation surfaces.

2) Roadmap execution & agile delivery

• Manage and refine the product backlog, balancing strategic priorities, technical debt, and customer needs.

• Partner with engineering to ensure crisp scope, acceptance criteria, rollout plans, and predictable

delivery.

• Lead agile ceremonies as needed: planning, stand-ups, reviews, retrospectives.

• Ship iteratively; ensure every release has success metrics and guardrails.

• Define and own the AI feature backlog for your vertical: where LLM integration adds genuine value to the

user journey, what the evaluation criteria are, and what fallback and failure handling looks like.

3) Metrics ownership & experimentation

• Own weekly reporting of funnel performance and drivers: what moved, why, and what we do next.

• Establish a steady cadence of experimentation — A/B tests, rollouts, UX improvements — with

measurable outcomes.

• Ensure measurement hygiene: instrumentation requirements are met and readouts happen on time.

• Define and track AI-specific product metrics: model-assisted conversion rate, AI feature adoption,

recommendation acceptance rate, and LLM output quality signals.

4) Stakeholder management & communication

• Act as the primary link between business leaders, tech teams, and external partners.

• Communicate roadmap, priorities, trade-offs, and performance insights to senior stakeholders.

• Partner across Australia, India, and the Philippines to maintain alignment on goals and delivery

milestones.

• Serve as the voice of the customer and ensure user-centric solutions are embedded in every release.

5) Partner ecosystem collaboration

• Work with Partnerships/Business Owners to shape partner onboarding priorities and improve partner performance.

• Own product readiness and launch gating: data capture, routing behaviour, UX flows, monitoring.

• Drive partner feedback loops into the roadmap: acceptance/rejection reasons, SLA breakdowns, failure modes.

6) AI-enabled product development

• Identify where LLM-powered features can meaningfully improve the consumer comparison journey: pre-filling eligibility data, surfacing contextual recommendations, simplifying complex offer comparisons, or reducing form friction.

• Write product requirements for AI features with the same rigour as any other feature: input/output definition, confidence thresholds, fallback behaviour, human-in-loop requirements, and success metrics.

• Collaborate with the AI & Automation SPM and engineering to ship AI features that are production-grade: instrumented, monitored, and with defined rollback criteria.

• Stay current on how LLM integration is being applied in consumer FinTech, insurance, and comparison product UX - bring relevant patterns into your vertical proactively.

• Use AI tools actively in your own product practice: research synthesis, PRD drafting, experiment

hypothesis generation, competitive analysis, and data interpretation.

7) Innovation & growth

• Use data, market trends, and customer behaviour to uncover opportunities that increase adoption and monetisation.

• Partner with UX/UI and Marketing to launch improvements that boost customer experience and

conversion.

• Benchmark performance against competitors and continuously evolve CIMET's comparison experience.

8) Quality & continuous improvement

• Ensure UAT readiness and validate outcomes before broad release.

• Monitor metrics: engagement, NPS where applicable, conversion, and lead quality.

• Collaborate with QA and DevOps to ensure stable, scalable releases and fast incident recovery.

• Maintain a feedback loop that translates insights into actionable roadmap items.

Candidate Criteria

Mandatory

• Strong Senior Product Manager — Vertical Products (Fintech, Growth & Conversion) Profile
• 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).
• 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.
• 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.
• Mandatory(Experience 4)- Must be fluent with AI — active, habitual use of AI tools (ChatGPT, Claude, Gemini, Perplexity, or equivalent) in daily product practice.
• 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).
• 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.
• 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.

Preferred

• 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.