Data Scientist

Ambition; Kuala Lumpur; On-site; MYR 120,000 - 180,000; Full time

Job summary

Ambition is seeking a Data Scientist for Credit Card Portfolio Analytics in Kuala Lumpur on a 12-month contract. You will drive analytics across the card lifecycle, build models, run experiments, and surface insights to influence business decisions.

You will collaborate with senior stakeholders and cross-functional teams, translating complex analyses into clear recommendations and dashboards using Tableau or Power BI. A strong ML and statistics background is essential.

Qualifications

• Master's degree or above in Data Science, Statistics, Mathematics, or related field.
• 5–8+ years in data science or advanced analytics, ideally within financial services.
• Experience in credit card analytics, lifecycle modelling, or marketing optimisation.
• Proficiency in SQL, Python, Spark, Hive or cloud-based analytics platforms.
• Hands-on experience with supervised and unsupervised machine learning techniques.
• Strong grasp of statistical inference, experimental design, and causal methods.
• Confident communicator able to engage senior, non-technical audiences.
• Exposure to fraud, credit risk, or payments analytics is a strong advantage.

Responsibilities

• Own analytical workstreams spanning acquisition, usage, retention, fraud, and credit risk.
• Build and deploy machine learning and statistical models to uncover portfolio opportunities.
• Design A/B tests and causal inference frameworks to evaluate strategies.
• Develop segmentation, propensity, and uplift models to support portfolio decisions.
• Analyse large-scale transaction datasets to surface actionable insights.
• Create performance dashboards using tools such as Tableau or Power BI.
• Translate findings clearly for both technical and non-technical stakeholders.

Skills

Data storytelling

Stakeholder communication

Machine learning

Statistical inference

Experiment design

Education

Master's degree or above in Data Science/Statistics/Mathematics

Tools

SQL

Python

Spark

Hive

Cloud-based analytics

Job description

Data Scientist | Credit Card Portfolio Analytics | Kuala Lumpur

If you're a data science professional with a passion for financial analytics, this is a standout opportunity. Join a globally respected payments environment and lead analytical workstreams that deliver real impact across a high-value card portfolio.

Why Apply?

Based in Kuala Lumpur on a 12-month contract, this role places you at the centre of credit card portfolio strategy - from acquisition and activation through to fraud and risk. You'll work alongside senior stakeholders and cross-functional teams, with your insights directly influencing business performance.

The Role

Driving analytics across the full card lifecycle, you'll build models, test hypotheses, and surface insights that matter.

• Own analytical workstreams spanning acquisition, usage, retention, fraud, and credit risk
• Build and deploy machine learning and statistical models to uncover portfolio opportunities
• Design A/B tests and causal inference frameworks to evaluate strategies
• Develop segmentation, propensity, and uplift models to support portfolio decisions
• Analyse large-scale transaction datasets to surface actionable insights
• Create performance dashboards using tools such as Tableau or Power BI
• Translate findings clearly for both technical and non-technical stakeholders

What We’re Looking For

A strong technical foundation paired with the ability to tell compelling data stories.

• Master's degree or above in Data Science, Statistics, Mathematics, or a related discipline
• 5-8+ years in data science or advanced analytics, ideally within financial services
• Experience in credit card analytics, lifecycle modelling, or marketing optimisation
• Proficiency in SQL, Python, Spark, Hive, or cloud-based analytics platforms
• Hands‑on experience with supervised and unsupervised machine learning techniques
• Solid grasp of statistical inference, experimental design, and causal methods
• Confident communicator able to engage senior, non-technical audiences
• Exposure to fraud, credit risk, or payments analytics is a strong advantage

Company Registration Number: 201301019088 (1048918-T)