Senior Data Scientist

Maya; Mandaluyong; On-site; PHP 1,200,000 - 2,400,000; Full time

Job summary

Maya is seeking a Senior Anti-Fraud/Security Data Scientist to safeguard financial integrity by building advanced fraud detection models and leading feature engineering efforts.

You will collaborate with risk, security, and engineering teams to deploy models into production, monitor performance for drift, and translate data into actionable prevention strategies across the organization.

Qualifications

• Bachelor’s degree in data science, CS, statistics or a related field.
• Strong proficiency in Python or R.
• Expertise in ML and statistical techniques for security and fraud.
• Experience with data mining, cleaning and feature engineering in security/fraud contexts.
• Domain expertise in fraud detection or cybersecurity threat detection.
• Knowledge of fraud detection methodologies including rule-based systems and anomaly detection.
• Experience with risk scoring, alert triage and investigation workflows.
• Production-ready modeling with monitoring and explainability considerations.
• Ability to work independently and in teams; collaborate with security and engineering.
• Minimum of 6 years of relevant experience in security/fraud data science.
• R&D experience is a plus; publishing in security/fraud venues is advantageous.

Responsibilities

• Feature engineering and selection to optimize model performance.
• Develop and evaluate fraud detection ML models with metrics and A/B tests.
• Analyze data to identify patterns and anomalies indicating fraud; provide actionable insights.
• Collaborate with engineering to deploy models into production and monitor for drift.
• Communicate complex concepts to technical and non-technical stakeholders.
• Mentor and guide data scientists to foster innovation and learning.

Skills

Python

R

Fraud detection

Machine learning

Data mining

Feature engineering

Anomaly detection

Time-series analysis

Graph analytics

Risk scoring

Collaboration

Explainability

Education

Bachelor’s degree in Data Science, Computer Science, Statistics, or related field

Job description

Overview

Senior Anti-Fraud/Security Data Scientist responsible for safeguarding financial integrity by developing fraud detection models.

Responsibilities

• Feature Engineering and Selection: Identify, extract, and engineer features from diverse data sources to discriminate between legitimate and fraudulent users. Conduct feature selection and dimensionality reduction to optimize model performance.


• Model Development and Evaluation: Develop and implement ML models to detect fraud; evaluate with relevant metrics and conduct A/B tests to validate effectiveness.


• Data-Driven Insights: Analyze data to uncover patterns, trends, and anomalies that indicate fraudulent behavior; generate actionable insights to enhance prevention strategies.


• Model Deployment and Monitoring: Collaborate with engineering to deploy models into production and establish monitoring to track performance and detect concept drift.

Additional Responsibilities

• Cross-Functional Collaboration: Communicate technical concepts to both technical and non-technical stakeholders; collaborate with risk analysts and operational teams to refine prevention strategies.


• Team Leadership: Mentor and guide data scientists, fostering a culture of innovation and continuous learning.

Required Qualifications

• Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field.


• Strong proficiency in Python or R programming languages.


• Expertise in machine learning and statistical techniques applicable to security and fraud (e.g., anomaly detection, unsupervised learning, supervised classification, time-series analysis, graph analytics, fraud risk scoring).


• Experience with data mining, data cleaning and feature engineering in security/fraud contexts.


• Domain expertise in fraud detection, financial crime, or cybersecurity threat detection.


• In-depth knowledge of fraud detection methodologies and best practices, including rule-based systems, anomaly detection and behavioral analytics.


• Familiarity with security controls and threat modeling.


• Experience with risk scoring, alert triage, and investigation workflows; ability to create production-ready models with monitoring and explainability considerations.


• Excellent problem-solving and analytical skills; strong attention to detail.


• Ability to work independently and as part of a team; strong collaboration with security, risk, and engineering stakeholders.


• Minimum of 6 years of relevant experience in security/fraud data science or closely related fields.


• Research and development experience is a plus; experience publishing or presenting in security/fraud venues is advantageous.


• Proficiency with security/fraud tooling and platforms is a plus.