Head Risk Analytics & Modelling

  • Jakarta, Indonesia
  • Full-time

Company Description

Indodana is a Fintech Lending platform from PT Artha Dana Teknologi which was founded in November 2017 and has been registered by OJK since March 2018. PT Artha Dana Teknologi provides loan services, installments without credit cards, HP credit and PayLater services.

Starting from the many requests from the public who failed to get financial product services by banks in Indonesia, PT Artha Dana Teknologi presented Indodana as a solution to make it easier for the public to access various financial products.

Job Description

  • Build credit risk modelling, analytics and reporting for retail customerStrategically hire, train, and manage risk teams
  • Partner with product team to determine how technology help to detect and reduce risk.
  • Analyze key metrics to determine risk.  
  • Mentoring and supporting junior members of the team will be a key part of the role, in both technical data manipulation skills and documentation delivery. 
  • Maintain the credit model platform which is utilised by the team and expected to provide advice and guidance on ongoing efficiency gains and new credit modelling methodology.

Qualifications

  • Have an undergraduate degree in an analytical discipline (e.g. maths, statistics), however other disciplines will be considered 
  • Have 5+ experience of working in credit / risk modelling for retail customers especially unsecured lending in bank or non bank 
  • Have significant experience of credit modelling using statistic tool such as R, Phyton SAS, SQL analytical tools or advanced MS Excel skills
  • Previous experience building unsecured lending credit risk modelling is highly prefered. 
  • Have strong verbal and written skills that ensure you can explain technical concepts to less technically focused colleagues and produce suitable documentation
  • Be organised, approachable, confident in engaging with stakeholders at all levels and a keen attention to detail
  • Experience of reporting in a Financial environment. 
  • Experience and/or understanding of data validation and verification techniques, advanced data manipulation techniques, development and production lifecycles, error handling, and automated reporting generation.