At TNG Digital, part of TNG Digital Group, we build the tech behind TNG eWallet, one of Malaysia's most used apps for payments and everyday life.

Millions of people rely on us daily, not just to pay, but to manage money, access financial services, and get things done faster and simpler.

Behind it all is a team that likes to question how things are done, move quickly, and try new ideas. If you enjoy solving real problems and seeing your work used at scale, you will feel right at home here.

What You'll Do:

A Risk Data Scientist in Fraud & Payment Risk is responsible for building and maintaining data-driven solutions to identify, analyze, and mitigate fraud and credit risks across TNGD's e-wallet platform. This role combines traditional data science with active use of AI-powered tools to accelerate modelling, analysis, and decision-making.

Build Models and Algorithms:

Design and calibrate risk rules and decision frameworks for fraud and credit risk mitigation across e-wallet products, including merchant risk monitoring and BNPL onboarding assessments.
Leverage AI-powered tools (e.g., LLM assistants, AI code copilots, generative AI) as part of the day-to-day analytics and modelling workflow — from exploratory data analysis and feature engineering to report generation and documentation.

Data Analysis and Risk Assessment:

Analyze large-scale transaction datasets to uncover fraud patterns, merchant risk indicators, and anomalous user behaviours using SQL-based analytics on cloud data platforms.
Apply statistical methods to quantify risk exposure, measure fraud impact, and establish data-driven thresholds for automated decisioning.

Process Improvement:

Continuously improve fraud detection pipelines — from data ingestion and feature engineering to rule calibration and decisioning — ensuring detection methods remain effective against evolving fraud tactics.

Collaboration and Mentoring:

Collaborate with risk operations, engineering, product, and BI teams to integrate detection solutions into production workflows and align risk strategies with business objectives.
Contribute to team knowledge development through mentoring junior colleagues on analytical methods, AI-augmented workflows, and code review best practices.
Propose data-backed strategies and rule enhancements to strengthen fraud and payment risk posture across the platform.
Partner with engineering teams to operationalize models and rules, ensuring reliable deployment and minimal false-positive impact.

Innovation and Learning:

Stay current with advancements in fraud detection, machine learning, and AI tooling — including emerging fraud techniques and new AI/ML applications relevant to fintech risk.
Proactively identify opportunities to apply AI-driven approaches and automation to enhance existing fraud prevention and risk assessment workflows.

Reporting and Communication:

Produce clear, actionable reports and dashboards to communicate risk findings, model performance, and strategy recommendations to stakeholders and senior management.
Translate analytical insights into concrete recommendations that influence risk strategy decisions and regulatory compliance.

Role Requirements:

Education:

A bachelor's degree or higher in computer science, data science, statistics, mathematics, machine learning, or a related quantitative field.

Experience:

A minimum of 1–2 years of experience in data analysis, statistical modelling, or machine learning, preferably in fintech, e-commerce, or financial services risk.

Technical Skills:

Strong proficiency in Python and SQL, with experience working on cloud data platforms (e.g., MaxCompute, BigQuery, or equivalent).
Experience with data visualization and BI tools such as Looker, Metabase, or similar platforms.

AI & Tooling Proficiency:

Demonstrated comfort using AI assistants (e.g., GitHub Copilot, ChatGPT, Claude) in daily work — from writing and debugging code to accelerating data exploration and automating repetitive analysis.
Ability to effectively prompt and interact with AI tools to produce high-quality analytical outputs, draft documentation, and prototype solutions rapidly.

Analytical Skills:

Ability to independently explore and analyze large transaction-level datasets, identify fraud patterns, and derive actionable risk insights.

Domain Knowledge (Nice to Have):

Familiarity with payment fraud typologies, merchant risk monitoring, mule detection, or credit risk assessment in a fintech or e-wallet context.
Understanding of regulatory frameworks relevant to e-money and digital payments (e.g., BNM guidelines).

Soft Skills:

Strong written and verbal communication skills, with the ability to present technical findings clearly to both technical and non-technical audiences.
An ownership mentality with strong initiative, intellectual curiosity, and a proactive approach to leveraging AI tools for productivity gains.

What you get

Work your way

Flexible working hours

Your wellbeing matters

Medical coverage, with option to include dependants
Extra leave for family and caregiving needs

Rewards that grow with you

Monthly lifestyle allowance via TNG eWallet
Long-term rewards for your contributions

Everyday support

Mobile and broadband reimbursement
Discounts and wellness perks

What it's like to work here

We care about people who take ownership, speak up, and want to make things better. Titles matter less than impact. Good ideas can come from anyone.

You will be working with people who are curious, practical, and not afraid to challenge each other in a good way.

Note: Only shortlisted candidates will be contacted.

Risk Analyst (Detection Team)

Analyst, Risk Data Scientist