About the Team:
We are Ryt Bank, proudly awarded as one of the five digital banking license winners by BNM in Malaysia. Our mission is to empower the underserved and unserved segments of the population by providing innovative and accessible banking solutions. As part of our team, you will play a critical role in shaping the future of digital banking, using cutting-edge technologies to deliver exceptional user experiences for Malaysians.
Our team consists of experienced digital banking and fintech innovators who are passionate about driving impactful, AI-powered innovation in the financial industry. Together, we are building a bank by Malaysians, for Malaysians.
About the Role:
We are looking for a Senior Data Scientist to strengthen the bank’s capability to detect and prevent fraud across the customer lifecycle.
You will work with large scale datasets, real time fraud signals, and evolving attack patterns to design detection strategies that are transparent, measurable, and effective. This role is critical in ensuring that fraud decisions are backed by data, tested through simulations, and continuously improved to balance risk control with a smooth customer experience.
Your work will directly help the bank avoid monetary losses, safeguard customers, and meet regulatory expectations.
Key Responsibilities
Build and maintain machine learning models and rule based systems to detect fraud in loans, payments, cards, and other customer activities.
Develop and run systematic backtesting and simulation to validate the effectiveness of fraud rules and models, and to minimise false positives.
Combine multiple fraud signals into actionable detection strategies that are explainable and measurable.
Analyse behaviour, transaction patterns, and documentation to uncover new fraud typologies, including synthetic identities, deepfakes, and manipulated documents.
Identify and mitigate emerging fraud threats such as deepfake videos and manipulated identity documents.
Collaborate closely with product, engineering, and fraud operations teams to deploy real-time fraud detection strategies.
Monitor and fine-tune model thresholds, feature sets, and decision logic based on performance metrics and evolving attack vectors.
Design experiments and simulations to measure the impact of fraud mitigation strategies on approval rates, fraud loss, and user experience.
Build and maintain dashboards, reporting pipelines, and automated alerts to track fraud KPIs across products.
Stay ahead of the curve on emerging fraud techniques specific to digital lending, payments, and synthetic identity creation.
What We’re Looking For
2–5+ years of experience in fraud analytics, data science, or risk modeling, ideally with exposure to digital banking, lending, or payments.
Strong skills in Python, SQL, and machine learning libraries (e.g. XGBoost, PySpark).
Experience with back testing and scenario simulation to evaluate fraud detection strategies.
Strong analytical thinking, with attention to detail and the ability to distill complex signals into actionable insights.
Understanding of loan fraud risk and fraud attack vectors in digital lending is a strong advantage.
Nice to Have
Experience in building fraud detection for unsecured personal loans, BNPL, or micro-lending.
Familiarity with graph analytics, anomaly detection, or identity verification solutions.
Awareness of compliance requirements and regulatory expectations for fraud, lending and payments (e.g., BNM, AMLA).
Why Join Us:
Be part of an award-winning digital bank project that is reshaping Malaysia’s financial landscape.
Collaborate with a team of passionate, driven, and talented innovators.
Drive impactful solutions that empower underserved and unserved communities.
Grow your technical skills in a dynamic and fast-paced environment that values learning and innovation.
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