At Maya, we don't just resolve customer issues—we work to understand why they happen and fix them at the source.
We're looking for a Product Analyst to shape what we build and how we prioritize across our Customer Care platforms. You'll use data to drive product decisions — identifying the highest-impact problems and making the case for how to solve them, whether through product improvements, AI automation, self-service, or better agent tooling.
This is not a reporting or dashboarding role. We need someone who uses data to drive product discussions — not someone who builds dashboards and waits for others to act on them.
If you're passionate about influencing what gets built and using data to make your case, this role is for you.
WHAT YOU'LL DO
In this role, you will be responsible for analyzing product, chatbot, and support data across all products and channels, building the metrics and business cases that drive prioritization, identifying opportunities to resolve customer issues as early as possible, and measuring the success of the team's initiatives.
Your responsibilities include:
Analysis & Root Cause Investigation
- Analyze data across all resolution channels: product usage and user behaviour, chatbot and AI conversation logs, and human agent support interactions, across all products and customer segments (consumer and enterprise).
- Connect product usage data to support outcomes. Identify the in-product journeys, error states, and drop-off points that lead to customers needing help.
- Analyze chatbot conversation data to identify where AI interactions fail, where customers drop off or escalate, and where conversation design or knowledge gaps reduce resolution effectiveness.
- Build and maintain a structured contact reason taxonomy that is specific enough to be actionable by product and engineering teams.
- Identify patterns and root causes: which products, features, or journeys generate disproportionate support load, and why. Distinguish between support problems and product problems.
- Track repeat contact patterns. Customers hitting the same issue multiple times signals a product gap, not a support gap.
Metrics & Measurement
- Define and maintain the care platform team's metrics framework, anchored to the team's north-star outcomes: customer satisfaction (CSAT), resolution effectiveness, and operational efficiency (cost-per-interaction, agent productivity).
- Establish baseline metrics across all products and channels, and track trends over time.
- Measure the success of the team's initiatives across all resolution channels. Close the loop on whether changes delivered the expected results and surface follow-up opportunities.
- Build and maintain dashboards and reporting that give the team and leadership real-time visibility into performance across chatbot resolution, self-service adoption, and agent efficiency.
- Track the effectiveness of process changes and operational improvements over time.
Prioritization & Business Cases
- Translate analytical findings into prioritized recommendations for the wider team, backed by data on volume, cost, customer impact, and feasibility.
- Segment issues by the best resolution channel: which are best addressed by in-product self-service, chatbot improvements, or better agent tooling.
- Identify and prioritize which support actions and issue types the chatbot should handle next, based on volume, resolution complexity, cost, and customer impact.
- Build cost attribution models that quantify each product's support burden. This creates accountability and visibility for product teams whose flows generate significant ticket volume.
- Build business cases for investment decisions: quantify the cost of inaction, the expected ROI of proposed improvements, and the trade-offs between competing priorities.
Self-Service & Product Quality Influence
- Identify opportunities where customers could resolve issues themselves through better in-app information, improved FAQs, clearer product flows, or proactive communication, rather than contacting support.
- Socialize findings with product managers across the organization and influence product quality improvements by presenting clear, data-backed business cases for change.
- Track the impact of self-service and product quality improvements on resolution rates to build momentum and demonstrate value.
Stakeholder Collaboration
- Work closely with the Customer Service Group (CSG) and other back-office teams to understand operational realities, validate analytical findings, and ensure recommendations are grounded in how support actually works.
- Partner with data science and AI teams to share conversation insights, validate chatbot performance, and inform priorities for chatbot capability expansion.
- Own the closed-loop feedback mechanism between support operations, the care platform team, and other product teams.
- Ensure recurring support issues are surfaced to the right product owners with enough context and data to drive action, including volume, cost, customer impact, root cause, and the upstream user behaviours that trigger them.
- Monitor CSAT trends and correlate with product changes, support process changes, and AI rollout to understand what is driving movement.
WHAT WE'RE LOOKING FOR
- Proven track record of influencing product roadmaps or prioritization through data-backed recommendations
- Strong product thinking: you go beyond surfacing problems to proposing solutions and identifying the right resolution path
- 5+ years in product analytics or similar role ideally in customer support, customer experience
- Strong SQL and data visualization skills (Tableau, Looker, Power BI, or similar)
- Proven ability to turn data into business cases and product recommendations
- Understanding of customer support operations and contact drivers
- Ability to connect data to real customer and agent experience—not just report metrics
- Strong communication skills to influence stakeholders across product, engineering, and operations
- Experience in FinTech, financial services, or payments is a strong advantage
WHAT SUCCESS LOOKS LIFE FOR YOU
- Product teams act on your analysis — you can point to shipped product changes that came from your recommendations
- Product owners understand and own their support cost burden because you made it visible and undeniable
- Top contact drivers have documented root causes with clear, actionable recommendations
- More customer issues are resolved through self-service and AI channels, driven by improvements you identified
- Every major initiative has a before-and-after measurement that proves whether it worked
- Your recommendations consistently lead to measurable CSAT and/or cost improvements
Why join Maya
You'll play a critical role in shaping how we scale customer support—by ensuring we're solving the right problems with the right solutions.
This is an opportunity to work across product, AI, and operations, and directly influence decisions that impact millions of users.
