Data Analyst - PayLater

Job Description

Job ID: MJ000120

Job Description

We are seeking a high-caliber Data Analyst who will serve as a key thinking partner to the business. Our focus is on growing a sustainable lending business by providing our customers with a responsible and convenient form of credit. In this role, you will partner closely with the product, fraudinvestigation, and engineering teams to detect, investigate, and prevent fraudulent activity in various stages of PayLater business, delivering actionable insights to help drive critical business decisions. If you're a team player with a passion for data and analytics, we'd love to have you on board!

Our techstack: BigQuery, dbt, Python, Github, Tableau, Looker Studio, Google Cloud Build,

Main Responsibilities

• Understand PayLater business, customer behaviour and fraud in lending and OTA settings
• Collaborate closely with PM and fraud investigation teams to monitor fraudulent behaviour, uncover emerging fraud schemes/attacker personas, translate insights into new features, scorecard updates, or prevention rules/models. Present findings clearly through written narratives, documents, dashboards and during stakeholder discussions.
• Conduct A/B and backtesting to evaluate new detection concepts
• Develop dashboards and alerts not just for monitoring, but to produce actionable insights that decision makers can use to steer the business.
• Develop ETL pipelines within a robust framework that is highly automated and does not require a large team to maintain and monitor
  
  

Qualifications

• 2-5 years of experience in analytics with at least a Bachelor's degree in a quantitative field.
• Proficiency in SQL and data visualization tools (e.g. Tableau, Looker, Power BI).
• Strong skills in Python or R for data analysis and modeling
• Experience with A/B testing and experiment design.
• Excellent communication skills to convey complex analyses to non-technical stakeholders.
• Ability to translate business problems into analytical solutions and actionable recommendations.
• Ability to build classification, anomalydetection, and timeseries models
• Familiarity with common fraud vectors (cashout fraud, identity theft, account takeover, synthetic identity etc). Experience in fintech industries/consumer lending business is preferred.
• Proficiency in Bahasa (preferred)