VP, Regional Fraud Data Analytics

About UOB

United Overseas Bank Limited (UOB) is a leading bank in Asia with a global network of more than 500 branches and offices in 19 countries and territories in Asia Pacific, Europe and North America. In Asia, we operate through our head office in Singapore and banking subsidiaries in China, Indonesia, Malaysia and Thailand, as well as branches and offices. Our history spans more than 80 years. Over this time, we have been guided by our values – Honorable, Enterprising, United and Committed. This means we always strive to do what is right, build for the future, work as one team and pursue long-term success. It is how we work, consistently, be it towards the company, our colleagues or our customers.

About the Department

The Compliance function is a strategic partner and a trusted business enabler to the Board and senior management. It is our responsibility to ensure that the Group continuously fulfils its regulatory obligations in today’s tight and dynamic regulatory landscape. To do that, we work closely with internal stakeholders to identify and to assess regulatory risks. This collaboration also includes developing practical solutions that integrate regulations into operational requirements as well as actively shaping and promoting stronger compliance culture and literacy in the Bank.

Job Responsibilities

Responsible for building Regional AFC Fraud analytical capabilities in response to external and internal requests adopting in house AFC analytical models into countries context. These include driving and providing guidance to country AFC Fraud analytics team when engaging with the business, GC/AFC, regulators etcto understand the needs and requirements, subsequently reflect these requirements in the AFC Fraud analytics models together with Group Modelling team/ Centralised Modelling Team. This role also need to ensure that the design and data architecture for the countries supported can support AFC Fraud analytics needs so that we can have a seamless deployment of AFC analytical models into production environment and compatibilities of country specific systems against the model to be deployed.

Data infrastructure and pipeline

•Create, build, and maintain the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of sources 
•Deliver high-quality, production-ready data pipelines that bring and cleanse data from source systems to support AFC Fraud model build
•Implement processes/systems to monitor data quality, and drive optimization, testing and tooling to improve data quality 
•Design and/or evaluates open source or vendor tools to be used for data lineage mapping
•Make sure info/data flow from system to system is correct
 

Model development and deployment

•Liaise closely with relevant stakeholders (e.g., Data Management Office) to identify and assess suitability of data for AFC models, and Business Analyst and ensure assembled data sets used for model build meet business requirements
•Design and implement advanced fraud detection models, rules, algorithms and dashboards using big data analytics tools such as Python, Hive, Spark, and Impala.
•Active participation in developing model narratives by providing inputs from a data perspective (e.g., data requirements, data availability)
•Active participation in ongoing testing of models/outputs during development, prior to more formal model validation by an independent team
•Create  model deployment pipeline to automate deployment of models in UOB’s environments/systems, and work closely with Data Scientist(s) in the Modelling team and other stakeholders (e.g., Group Technology and Operations) to ensure models are production ready 
•Ensure the seamless deployment of new AFC Fraud analytics solutions and models without breaking anything or creating unintended effects in the production pipeline
 

Enhancement and support

•Identify, design, and implement enhancements for internal process related to data (e.g., optimizing data delivery, re-designing infrastructure for greater scalability) to improve data reliability, efficiency, and quality
•Performs data analysis required to troubleshoot data related issues and support the resolution of raised data issues 
•Support UOB in adopting and/or migrating to new production environment, if needed

Job Requirements

•5 –10 years of experience working in tech industry and 3 –5 years of experience in a data engineering and/or data operations role, ideally in the financial services industry
•Experience in fraud analytics or risk management is a plus
•Bachelor’s degree in Computer / Data Science, Computer Engineering, IT, Statistics or equivalent
•Proficiency in Python/Spark to code complex data engineering use cases
•Experience in data engineering tools such as Hive/Impala, Oozie, Hadoop, etc. 
•Experience with schema design and dimensional data modeling
•Experience with robotic process automation (RPA) and their use in disseminating analytics outcomes
•Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement
•Experience in deploying and scaling ML/AI models
•Familiarity with Agile methods and tools like JIRA
•Ability to create and maintain production ready data pipeline and deployment pipeline
•Strong people skills and take a big picture approach to planning
•Strong communication skills to interact with data scientists, business end-users, and possibly external vendors to design and develop data solutions
 

Be a part of UOB Family

UOB is an equal opportunity employer. UOB does not discriminate on the basis of a candidate’s age, race, gender, color, religion, sexual orientation, physical or mental disability, or other non-merit factors. All employment decisions at UOB are based on business needs, job requirements and qualifications. If you require any assistance or accommodations to be made for the recruitment process, please inform us when you submit your online application.

Apply now and make a difference.

Competencies

1. Strategise2. Engage3. Execute4. Develop5. Skills6. Experience