{"id":1318410,"url":"https://alion.io/job/gotymex-product-analyst","title":"Product Analyst","company":{"id":3827705,"name":"GoTymeX","domain":"gotymex.com","url":"https://alion.io/company/gotymex","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":75,"open_postings":10,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-05T05:45:15Z"}},"role":"Analytics","role_family":"Analytics","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Ho Chi Minh City, Vietnam"],"countries":["VN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":10000,"max_usd":27000,"period":"year","method":null,"sample_n":5640},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AWS","optional":false},{"name":"Claude","optional":false},{"name":"Cursor","optional":false},{"name":"Databricks","optional":false},{"name":"Java","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Agile","optional":true},{"name":"AppsFlyer SDK","optional":true},{"name":"Braze","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-09-16T00:00:00Z","employer_posted_date":"2026-09-16","last_verified_at":"2026-10-05T23:10:01Z","board_verified":true,"closed_at":null,"days_open":20,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":20},"description":"About GoTymeX\nGoTymeX is the product, technology and data analytics hub of GoTyme Group. At GoTymeX, we're reimagining digital banking across emerging markets, across South Africa and Asia. Our mission is to unlock human potential, and we aim to do so through the development of transformative financial services that empower individuals and small businesses. We believe in the power of digital in building products that make a real difference in our customers' lives. Today, our products serve more than 22 million customers.\nAbout the Product-Led Growth Portfolio\nProduct-Led Growth is the horizontal growth layer of GoTyme Group. Where our other product portfolios (payments, shopping, savings and investments, lending, cards) build banking capability, Product-Led Growth owns whether customers discover it, adopt it, and keep using it. Our mandate runs the full customer journey, from app install through to a customer who transacts every month, across our retail banking apps in the Philippines and South Africa.\nThe portfolio runs as three product pods:\nCustomer Lifecycle Engagement, which owns all app-wide notifications and lifecycle campaigns \nInsights and Personalisation, which owns the app home screen and the primary in-app action surfaces, the most-viewed real estate in the product \nLoyalty and Rewards, which owns the rewards programme and its in-app experience \nProduct-Led Growth is also the Group's designated data champion: the portfolio expected to run the most experiments and to set the standard for daily, data-driven decision-making.\nWhere This Role Sits\nYou will report directly to the Director of Product, Product-Led Growth, and work day-to-day alongside the Product Owners, Engineers, Designers and Business Analysts in the pods. You will carry a dotted line to the Head of Data and Analytics, giving you deep technical mentorship while remaining fully embedded in the product teams. You will collaborate regularly with in-country data, marketing and customer experience analysts in the Philippines and South Africa.\nWhy Work with GoTymeX\nInnovation-driven environment: Work with the latest technologies including serverless AWS, AI-augmented engineering, microservices, Java and Python. \nInternational and collaborative culture: Be part of a dynamic team that values collaboration, continuous learning, and personal growth. \nCompetitive benefits: A comprehensive benefits package plus structured professional development. \nLearning and development: Access to technical seminars, conferences, career talks, and overseas training. \nImpactful work: Contribute to projects that directly influence financial empowerment and access in emerging markets. \nAbout This Role\nThe Senior Product Analyst is the analytical engine of the Product-Led Growth pods. This is not a reporting role, it is a product intelligence function. You will investigate why customers behave the way they do at each step of the adoption journey, establish what the product is actually doing versus what we assume it is doing, and give the pods a clear view of where the next point of growth sits. You will also be the team's guide on the experiments we run, shaping A/B and multivariate tests from hypothesis through to readout so that the pods learn quickly and act on evidence.\nKey Responsibilities\nInvestigation and Deep Dives\nOwn the analytical narrative for your pods: what changed, why it changed, and what it means for the roadmap \nRun structured deep dives across the full adoption journey, from app install and onboarding through account funding, first transaction, repeat transacting and sustained monthly activity, and size the drop-off at each step \nSegment and profile cohorts by market, tenure, acquisition source and product mix in collaboration with country data teams to identify where growth is concentrated and where it is leaking \nGenerate root-cause hypotheses on performance movements early and bring them to the pod proactively \nExperimentation\nGuide the pods on experiment design, covering A/B tests, multivariate tests and holdout design, and advise on which method fits the question being asked \nOwn the statistical rigour of the programme: hypothesis framing, sample sizing and duration, guardrail metrics, and the treatment of multiple comparisons and interaction effects in multivariate tests \nRead out results with a clear call on what we learned, what we should ship, and what we should stop \nBuild a shared record of experiments and outcomes so that learning compounds across pods and markets rather than being rerun \nInsight Generation with Product Owners\nPartner with the Product Owners across all three pods to turn analysis into clearly shaped problems and prioritised opportunities \nGo beyond the numbers: state what the data shows, what you believe it means, and what you would do next \nSurface insights the pods did not know to ask for, such as behavioural patterns among the most engaged customers, signals of notification fatigue, and rewards redemption behaviour \nWork with in-country teams to validate and localise findings in both markets before they reach a roadmap decision \nProduct Understanding\nDevelop first-hand working knowledge of both retail banking apps, including onboarding and identity verification, account funding, domestic transfers, QR and proxy-based payments, savings products, cards and rewards mechanics \nUnderstand the nuances that shape the data: market-specific payment rails and flows, regulatory differences, app version fragmentation, and how each pod's surfaces interact with one another \nEmbed in sprint ceremonies and product rituals, and define the measurement framework for a feature before it ships rather than after \nEventing and Instrumentation\nAudit current event tracking across both apps and identify gaps, duplication and inconsistency in the tracking plan \nDefine event taxonomy and naming standards, and work with Engineering to specify the events each pod's surfaces require \nRun pre-launch data readiness checks so that every release is measurable from day one, including the events an experiment needs to be readable \nOwn data quality for growth metrics: reconcile the product analytics layer against the data warehouse, document known variances, and raise the level of trust in the numbers the pods act on \nDashboards and Daily Decision Support\nBuild and maintain self-serve dashboards that the pods use daily, covering funnel health, step-by-step conversion, notification and campaign performance, personalisation impact, and rewards engagement \nDesign dashboards for decisions rather than for completeness. Each view should answer a question a Product Owner asks every week \nMake live experiment performance visible to the pods, so that teams can see what is in flight and how it is tracking without asking \nUse modern AI tooling (Claude, Cursor, Databricks AI) to compress the time from question to answer. In this environment, fast insight beats slow perfection \nRequirements\nTechnical Skills\nSQL: expert level, including complex queries, window functions and optimisation at scale \nPython or PySpark: proficient for data manipulation, statistical analysis and automation \nProduct analytics: hands-on depth in Mixpanel or an equivalent such as Amplitude or Heap, including funnel, retention and cohort analysis \nBI and data warehouse: Databricks primary, Tableau or similar also acceptable \nInstrumentation: practical experience with a customer data platform such as Segment, and with defining and shipping a tracking plan alongside engineers \nStatistics and experimentation: hypothesis testing, cohort analysis, and hands-on design and analysis of A/B and multivariate tests \nAI-augmented analytics: demonstrated daily use of AI tools to accelerate insight generation and automate workflows. This is an expectation rather than a nice-to-have \nDomain Experience\n5+ years in data or analytics, with consumer digital product, fintech or banking experience required \nGrowth, lifecycle or engagement analytics background strongly preferred. Activation rates, cohort retention curves, resurrection and net churn should be familiar working vocabulary \nExperience analysing lifecycle messaging or CRM performance in a platform such as MoEngage, Braze or Iterable is a meaningful advantage \nExposure to mobile attribution platforms such as AppsFlyer, and to feature flagging or experimentation platforms such as LaunchDarkly, Eppo or Optimizely, is an advantage \nCore Competencies\nEnd-to-end ownership: raw data through to recommendation, accountable for whether it is right \nInsight over output: a clear view on what to do, supported by the analysis, every time \nSpeed: good-enough-fast beats perfect-and-late in a high-velocity build environment \nProactivity: you spot the question before it is asked and flag the signal early \nProduct instinct: you use the product, notice what is off, and can hold a design conversation as comfortably as a data one \nData literacy leadership: you raise the analytical bar of everyone around you, not only your own output \nPreferred Qualifications\nBachelor's or Master's degree in Mathematics, Statistics, Economics, Computer Science or a related quantitative field \nExperience in a multi-market or multi-country product environment \nBackground in a high-growth startup or digital bank \nExperience working with product teams operating on a shared metric model \nBenefits\nMeal and parking allowance are covered by the company.\nFull benefits and salary rank during probation.\nInsurances such as Vietnamese labor law and premium health care for you and your family.\nLearning and Development: Access to SMART goals, technical seminars, conferences, career talks, and overseas training to accelerate your career advancement and ensure continuous growth..\nValues-driven, international working environment and agile culture.\nOverseas travel opportunities for training and working related.\nInternal Hackathons and company’s events (team building, coffee run, blue card…)\n13th-month salary and performance bonus and share award.\n15-day annual + 3-day sick leave per year from the company.\nWork-life balance 40-hr per week from Mon to Fri.","description_format":"text","description_chars":10147,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Continuous learning","Professional 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