Risk Engineering Architect - Payment (ML/DS background)

Michael Page; Daerah Khusus Ibukota Jakarta; On-site; IDR 200,000,000 - 300,000,000; Full time

Job summary

An established industry player is seeking a seasoned professional to lead the development of innovative risk management solutions. This role involves collaborating across teams to create real-time systems and scalable frameworks that effectively mitigate fraud. You will leverage your deep understanding of risk analysis and machine learning to enhance prediction accuracy and refine adaptive risk scoring. Join a dynamic environment where your expertise will contribute to cutting-edge technology solutions that shape the future of financial security. If you're passionate about driving impactful change in the payment sector, this opportunity is tailored for you.

Qualifications

• Expertise in building scalable risk systems with a focus on fraud prevention.
• Strong analytical skills with a background in risk and fraud analytics.

Responsibilities

• Develop real-time risk decisioning systems and scalable frameworks.
• Create machine learning models for fraud detection in payments.

Skills

Backend Engineering

Risk Analysis

Fraud Detection

Anomaly Detection

Problem-Solving

Education

Bachelor's Degree in Computer Science or related field

10+ years in Risk Engineering or Fraud Analytics

Tools

Machine Learning

Time-Series Analysis

Job description

About Our Client

Our client is a leading payment company integrated with various integrations across businesses.

Job Description

• Collaborate cross-teams & departments to develop real-time risk decisioning systems.
• Create scalable frameworks that convert risk strategies into engineering solutions.
• Define best practices for monitoring, detecting, and mitigating fraud.
• Create machine learning models for fraud detection in payments.
• Use time-series methods to anticipate risk and fraud patterns.
• Partner with data scientists to enhance prediction accuracy.
• Build clear workflows for handling compromised accounts (e.g., ATO).
• Define resolution steps, exit criteria, and user recovery processes.
• Improve fraud response while maintaining a smooth customer experience.
• Investigate false positives/negatives in detection systems.
• Continuously refine rules and models using live feedback and develop adaptive risk scoring to stay ahead of emerging threats.

The Successful Applicant

• Strong backend engineering or real-time systems experience.
• Deep understanding of risk analysis, fraud detection, and anomaly detection and familiarity machine learning for fraud prevention and forecasting.
• Proven track record in building scalable, automated risk systems, with 10+ years in risk engineering, fraud analytics, or security engineering.
• Solid experience in risk, fraud, and payment security.
• Skilled in working across risk, engineering, and data teams.
• Strong analytical and problem-solving mindset.

What's on Offer

Opportunity to create cutting-edge technology solutions for risk management.
Grow risk analytics practices for diverse financial technology product portfolio.