Credit Risk Strategy Analytics Role - Bank

Leadership pos. into Credit Risk Analytics!

Responsibilities

- Conduct portfolio analytics and deep dives as required by country/regional/Group Retail Credit. Develop credit strategies across the customer lifecycle of Origination, Portfolio Mgmt and Collections.

- Create insights for Retail Risk by studying portfolio trends, performing necessary analytics such as segmentation, profiling, cut-off setting, stress testing, sensitivity analyses

- Timely identification of shifts in portfolio risk profile and re-aligning risk-measurement tools for effectiveness and continuously enhance data-driven credit decisions

- Provide analytical support to various Risk Reviews, perform adhoc analytical requests to support policy changes

- Participate in selective strategic initiatives to build and enhance Group's in-house analytics capabilities; Research on emerging trends in analytics industry

Embed Bank's risk management and decisioning framework in day-to-day work, ideate / self-initiate necessary analytics to drive portfolio performance

- Supervise and coach team members for effective project delivery

Competency expected in role

1. Strong logical reasoning ability. Ability to perform various industry-standard complex statistical analysis in relation to credit risk problems

2. Ability to work with data organized across platforms and formats (DB2 tables, SAS datamarts, Excel spreadsheets, text files)

3. Ability to blend in existing BAU analytical projects and processes in a seamless manner. Ability to bring out meaningful and actionable insights from unstructured data

4. Ability to lead team members across multiple project assignments, while delivering on own projects

Skill Sets required:

- Advanced degree (preferably Masters) in a Quantitative Discipline (e.g. Math, Stat, Eco, Engineering, Finance, MBA)

- 10 years +yearsof relevant work experience, with 3 years experience of managing people / teams

- Cross-functional project management experience is a MUST

- Proficiency in SAS is a MUST with proven experience of working with large, complex data.

- Knowledge of other analytical tools such as R, Python, Tableau etc will be a PLUS

- Sound knowledge of Financial Services products, in particular Retail Banking, is a MUST including a good understanding of product economics

- Sound knowledge of Credit Risk is a MUST with ability to perform related data analysis