About Lucidya

Lucidya is an AI-native platform for customer experience (CX) intelligence that helps organizations understand their customers and take action across the entire customer lifecycle.

Our platform brings together products including Social Listening, OmniServe, Survey, and AI Agent, helping enterprise organizations across the region turn customer data into better experiences and measurable business outcomes.

As we continue to scale our products, customers, and AI capabilities, the amount of data we generate is growing rapidly. The opportunity is no longer simply to report on what happened - it's to understand why it happened, what it means, and what we should do next.

Why this role matters

We're looking for a Senior Product Analyst who can help make data a genuine part of how Product operates.

This isn't a role where you'll wait for a Product Manager to ask for a dashboard or pull a report after a decision has already been made. You'll be expected to get close to the product, understand how customers actually use it, spot patterns others may have missed, and bring insights to the table before someone asks for them.

You'll operate at two levels.

At the product and feature level, you'll help teams understand whether customers are discovering, adopting, engaging with, and getting value from what we're building. You'll identify friction, investigate unexpected behaviour, evaluate experiments, and help teams decide what to improve, scale, reposition, or stop.

At the leadership level, you'll connect those individual product signals to the bigger picture - helping leadership understand product performance, emerging opportunities, risks, and where we should invest next.

The strongest person in this role will be able to move comfortably between a SQL query, a conversation with a Product Manager, and a leadership presentation - and translate the same data into something meaningful for each audience.

What you'll do

• Turn product data into decisions. You'll go beyond reporting what happened to understand why it happened, what it means, and what Product should do differently as a result.
• Help teams define what success actually means. You'll work with Product Managers and other stakeholders to establish meaningful KPIs, success metrics, and measurement plans before features and initiatives are launched.
• Understand how customers use our products. You'll analyse journeys, funnels, cohorts, adoption, engagement, retention, churn, and behavioural patterns to identify where customers are succeeding and where they're getting stuck.
• Evaluate whether our products are delivering value. You'll assess feature performance and help teams make evidence-based decisions about whether to improve, scale, reposition, or discontinue initiatives.
• Bring insights into product discovery. You'll use quantitative evidence alongside customer, product, and market context to help teams understand which problems are worth solving and where the biggest opportunities exist.
• Challenge assumptions with evidence. When a team believes a feature is working, adoption is strong, or customers are behaving a certain way, you'll be comfortable asking, “What does the data actually tell us?” — constructively and without creating friction.
• Identify opportunities before you're asked. You won't wait for a stakeholder to request a report. You'll proactively investigate anomalies, emerging trends, underperforming areas, and changes in customer behaviour and bring forward the ones that matter.
• Support experimentation. You'll help teams define hypotheses, success criteria, and measurement approaches, then analyse experiments and A/B tests to determine what the evidence tells us.
• Make complex analysis easy to understand. You'll turn large datasets and complicated analysis into clear stories, visualisations, recommendations, and decisions that make sense to both technical and non-technical audiences.
• Give leadership a clear view of product performance. You'll create reporting that connects detailed product and feature performance to broader business objectives, highlighting progress, drivers, risks, and recommended actions rather than simply presenting numbers.
• Help Product become more self-sufficient with data. You'll build repeatable analytical frameworks, dashboards, documentation, and self-service resources so teams don't need to come to you for every question.
• Raise data literacy across Product. You'll coach Product Managers on metrics, analysis, and interpretation, and use workshops, office hours, and day-to-day collaboration to make better use of data part of how the team works.
• Build trust across teams. You'll work closely with Product, Design, Engineering, and business stakeholders and influence decisions without relying on formal authority.

What success looks like

In your first 3-6 months, you'll be building trust. Product teams know you as someone who understands the product, asks the right questions, and brings useful insights rather than simply producing reports.

As you establish yourself, you'll start changing how decisions are made. Product Managers are defining meaningful success metrics earlier, teams are using evidence to evaluate their decisions, and you're proactively surfacing opportunities and risks rather than responding to reporting requests.

Ultimately, success means Product becomes more intelligent. Leadership has a clear view of what's driving product performance, teams understand why customers behave the way they do, and decisions about priorities and investment are increasingly based on evidence rather than assumptions.

The shift we're looking for is simple:

From “What happened?” ? “Why did it happen?” ? “What should we do about it?”

Requirements

Who you are

• You're naturally curious. When you see an unexpected number, you don't just add it to a report — you want to know what's behind it.
• You think commercially, not just analytically. You understand that an interesting insight isn't necessarily a useful one. You can connect product behaviour to customer value, business performance, and investment decisions.
• You don't need to be given the question. You're comfortable starting with an ambiguous problem, figuring out what needs to be understood, and determining what analysis will actually help answer it.
• You can zoom in and out. You can spend time investigating why a particular feature's adoption dropped while still understanding how that finding connects to the wider product strategy.
• You're willing to challenge people. You don't simply validate what a stakeholder already believes. You're comfortable presenting evidence that contradicts an assumption, while doing so in a way that builds trust rather than defensiveness.
• You know that being right isn't enough. Your analysis only creates value if people understand it and act on it. You can turn complex findings into a simple story and a clear recommendation.
• You're a strong communicator. You can explain an analytical finding to a Product Manager, work through the technical detail with an Engineer, and present the business implication to leadership.
• You're proactive and self-directed. You don't wait for a ticket to appear in your queue. You notice something worth investigating and take ownership of finding out what it means.
• You're comfortable with ambiguity and a changing environment. Not every question will have a clean dataset or obvious answer. You know how to work with imperfect information while being transparent about its limitations.
• You have 4+ years of experience in product analytics, digital analytics, business intelligence, or a closely related field, with experience working directly with digital Product teams.
• You're technically strong. You can independently use SQL to explore and analyse large datasets and are comfortable working with product analytics or business intelligence platforms such as Mixpanel, Heap, Pendo, Metabase, Amplitude, Looker, Tableau, Power BI, or similar tools.
• You understand product metrics deeply, including funnels, journeys, cohorts, retention, engagement, adoption, churn, and customer behaviour.
• You can translate ambiguous business questions into structured analysis and understand the difference between finding a correlation and understanding what it actually means.
• You're comfortable working remotely and can communicate effectively across teams without relying on being in the same room.
• You have professional working proficiency in English.

Nice to have

• Experience designing or evaluating A/B tests and experiments.
• Experience with statistical analysis.
• Familiarity with modern data stacks, data warehouses, transformation tools, event tracking, analytics instrumentation, or data governance.
• Experience building or improving a product analytics function or data culture.
• Experience presenting product recommendations to senior leadership.
• Working knowledge of Python or R.
• Experience working with B2B SaaS, enterprise products, AI products, or complex data-driven platforms.

What the hiring process will look like

• Recruiter screening — An initial conversation about your experience, motivations, and what you're looking for next.
• Analytics interview — A deeper discussion around your product analytics experience, technical capability, and how you approach ambiguous analytical problems.
• Case study / analytical exercise — You'll be given a product or business problem and asked to analyse the available information, identify the key insights, and explain what you would recommend.
• Presentation interview — You'll meet Product and/or business stakeholders to explore how you communicate insights, challenge assumptions, and influence decisions based on the case study.
• Leadership / culture interview — A final conversation focused on how you operate, collaborate, handle ambiguity, and contribute to a high-performing team.