Total 3 system analyst job vacancies in Manufacturing / Production category in Overseas

About GoTymeX

GoTymeX 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.

About the Credit Card Product

We are building a group-level credit card product — launching across the Philippines and South Africa — on a modern global stack. The analytical function at the heart of this programme has never existed before. We’re creating it now — and we’re looking for the person to build it from zero with us.

Where This Role Sits

You will report directly to the Director of Product, and work day-to-day alongside the Credit Card Product Owners, Engineers, Designers and Business Analysts. You will carry a dotted-line to the Head of Data & Analytics, giving you deep technical mentorship while remaining fully embedded in the product team. You will collaborate regularly with in-country data and credit risk analysts across the Philippines and South Africa.

Why Work with GoTymeX

• Innovation-driven environment: Work with the latest technologies including Serverless aspects of AWS Cloud, AI-augmented engineering, microservices, Java, and Python
• International and collaborative culture: Be part of a dynamic team that values collaboration, continuous learning, and personal growth
• Competitive benefits: Enjoy a comprehensive benefits package, opportunities for professional development (see below) • Learning and development: Access to technical seminars, conferences, career talks, and overseas training to accelerate your career advancement and ensure continuous growth
• Impactful work: Contribute to projects that directly influence financial empowerment and access in emerging markets

About This Role:

The Senior Product Insights Analyst is the analytical engine of this team, owning the narrative behind every product decision. This isn’t a reporting role. It’s a product intelligence function. You’ll build the analytical foundation for our credit card business, identify what’s working and what isn’t, and tell us — with confidence — what to do about it.

Key Responsibilities

Product & Business Performance

• Own end-to-end performance analytics: activation, engagement, spend, repayment, and retention across all markets
• Track and interpret key KPIs — NPS, activation rates, digital adoption, unit economics (interchange, fee revenue, loyalty costs, ECL contribution) — and translate findings into clear product and commercial storylines
• Monitor portfolio health: revolve rates, payment behaviour, limit utilisation, and cohort evolution — with forward-looking commentary, not just snapshots
• Identify performance inflection points early and generate root-cause hypotheses — don’t wait to be asked

Funnel, Onboarding & Product Development Lifecycle Support

• Analyse the full acquisition and onboarding funnel — from application through activation and first spend — and size improvement opportunities
• Embed in sprint ceremonies and product rituals; define measurement frameworks for new features before they go live
• Provide pre-launch data readiness checks and post-launch performance reads for every release
• Support A/B testing and experiment design to evaluate product and go to market initiatives Insight Generation & Strategic Recommendations
• Go beyond the numbers — synthesise analysis into conclusions, flag strategic implications, and propose concrete product or commercial actions
• Proactively surface insights the team didn’t know to ask for: behavioural patterns, market signals, competitive benchmarks • Support go to market strategy, value proposition testing, and loyalty/rewards design with databacked recommendations
• Partner with in-country analytics and credit risk teams to validate and localise findings across the Philippines and South Africa Dashboards, Tooling & AI Utilisation
• Build and maintain self-serve dashboards in Databricks and Mixpanel covering business performance, funnel health, portfolio trends, and unit economics
• You’ll use the best of modern AI tooling — Claude, Cursor, Databricks AI — to compress the time from question to answer. In our environment, fast insight beats slow perfection
• Contribute to analytics best practices, documentation, and data quality standards across the Credit Card programme

Insight Generation & Strategic Recommendations

• Go beyond the numbers — synthesise analysis into conclusions, flag strategic implications, and propose concrete product or commercial actions
• Proactively surface insights the team didn’t know to ask for: behavioural patterns, market signals, competitive benchmarks • Support go to market strategy, value proposition testing, and loyalty/rewards design with databacked recommendations
• Partner with in-country analytics and credit risk teams to validate and localise findings across the Philippines and South Africa Dashboards, Tooling & AI Utilisation
• Build and maintain self-serve dashboards in Databricks and Mixpanel covering business performance, funnel health, portfolio trends, and unit economics
• You’ll use the best of modern AI tooling — Claude, Cursor, Databricks AI — to compress the time from question to answer. In our environment, fast insight beats slow perfection
• Contribute to analytics best practices, documentation, and data quality standards across the Credit Card programme
  

Technical Skills

• SQL: expert-level — complex queries, window functions, optimisation at scale
• Python / PySpark: proficient for data manipulation, statistical analysis, and automation
• BI / Visualisation: Databricks and Mixpanel (primary); Tableau or similar also acceptable
• Statistics: hypothesis testing, cohort analysis, and experimental design
• AI-augmented analytics: demonstrated daily use of AI tools to accelerate insight generation and automate workflows — this is an expectation, not a nice-to-have

Domain Experience

• 5+ years in data/analytics; financial services, fintech, or banking required
• Lending domain strongly preferred — credit card, BNPL, or personal loans. Terms like revolve rate, vintage analysis, and ECL contribution should be second nature, not new vocabulary
• Exposure to credit risk concepts (vintage analysis, ECL, delinquency) is a meaningful advantage
• Experience with customer lifecycle analytics: acquisition, activation, engagement, retention, and churn

Core Competencies

• End-to-end ownership: raw data to recommendation — accountable for whether it’s right
• Insight, not just analysis: “here’s what the data shows, here’s what I think it means, here’s what I’d do” — every time
• Speed: good-enough-fast beats perfect-and-late in a high-velocity build environment
• Proactivity: you spot the question before it’s asked and flag the signal before it becomes a problem
• Data literacy leadership: you raise the analytical bar of everyone around you — not just your own output

Preferred Qualifications

• Bachelor’s or Master’s in Mathematics, Statistics, Economics, Computer Science, or related quantitative field
• Experience in a multi-market or multi-country product environment
• Familiarity with Visa/Mastercard reporting tools or card management system platforms
• Background in high-growth startup or digital bank environments

• Competitive salary that recognises your talent and potential
• Performance-based bonus, including stock-based incentives for high performers
• Conferences and expert-led training to accelerate your career
• Flexibility to work from home or the office (HCMC-based, hybrid)
• Generous annual leave allowance, paid birthday leave, and additional days off
• Meal and parking allowances
• Premium healthcare for you and your family
• Overseas travel opportunities — Philippines, South Africa, and group offices

This role supports Open Content Product Operations by delivering high-quality analysis, reporting, and performance visibility against clearly defined requirements. You will work with large, varied datasets to produce robust, trusted outputs that inform operational and product decision-making. 

A core focus of the role is working with Open Content data — data spanning multiple sources, formats, markets, and use cases — and applying strong technical skills and data judgement to select appropriate data, ensure quality, and build reliable reporting, primarily using Power BI. 

The role also contributes to improving efficiency by reducing manual reporting and data preparation, through a combination of building automation directly where possible and working with technology or data engineering teams to implement more scalable solutions.

Key Tasks / Responsibilities:

• Deliver analysis, metrics, and reporting against clearly defined output requirements, working closely with Product Operations and Product Management 

• Identify, extract, clean, and structure data from available sources to create accurate, consistent datasets 

• Build, maintain, and enhance Power BI dashboards and reports, including data models, measures, and recurring reporting outputs 

• Create and maintain trusted business metrics and KPIs, ensuring clear definitions, documentation, and transparency around assumptions or limitations 

• Analyse open content data to support coverage measurement, performance tracking, and operational insights 

• Identify manual, repetitive, or inefficient reporting and data processes and drive improvements through automation, including building automation directly and working with data, platform, or engineering teams to raise tickets and support the creation of APIs, feeds, or data pipelines 

• Collaborate with cross‑functional stakeholders (e.g. Product, Operations, Finance, Technology) to gather inputs, align definitions, and ensure reporting accuracy 

• Produce clear supporting documentation and summaries to enable effective understanding and reuse of data and reports 

• Adhere to data governance, privacy, and quality standards 

We are looking for a meticulous and curious Product Analyst to join our Data Governance team, with a focus on providing robust analysis of our data source quality, defining metrics and reporting to generate actionable insights that drive decision making and trust in the data that powers our products and services.

As we expand our data sources, we need to understand their relative quality and depth to help us prioritise and harmonise product data in a way that provides the most insightful benefit for our clients. This role will work cross-functionally across our product teams and with key internal stakeholders, providing regular updates as well as targeted support and insight as required.

The Label Insight Data Governance Team, situated in our Product Team, is dedicated to ensuring the quality, integrity, and usability of data across the organization. Comprising three specialized teams—Attribute Health, Source Quality, and Data Support—it works collaboratively to uphold robust data standards, validate AI-generated outputs, and provide responsive, client-focused support. Together, the team enables scalable, trustworthy, and insight-ready data that powers our products and services.

Key Responsibilities:

· Evaluate the relative and overall quality of our data sources including data produced by AI models, web scraping, manual coding, and other coded sources

· Define and implement metrics that provide actionable insights to both improve data reliability and quality, and help support prioritisation and harmonisation of our data sources

· Provide consistent reporting on data source quality – using dashboards and reports to communicate trends and issues, and proposed actions to address

· Identify anomalies, patterns, and potential risks in these data sources, with a key focus on ensuring the confidence of AI-generated data

· Design and implement methods to detect outliers, inconsistencies, and unexpected trends in data sources

· Investigate root causes of anomalies and collaborate with technical teams and key stakeholders to address underlying issues

· Support the application of validation rules, checks, and metrics to monitor data quality over time

· Collaborate with data scientists and engineers to refine AI outputs based on quality findings

· As needed, support taxonomy recognition tasks to ensure our attributes achieve the required quality thresholds