AVP, Group Risk and Decision Making, Group Retail

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

The role is based in Singapore and will report to the Group Head of Risk and Decision Management. The Candidate is responsible for providing data science and analytics support to PFS, BB and PB segments within Group Retail, and working closely with Group Compliance and Business Compliance units in meeting UOB’s Compliance objectives and use cases. In particular, the role is expected to inject advanced statistical and machine learning models and big data usage into the onboarding, transaction monitoring and screening processes.

Responsibilities

• Develop new and enhance / fine-tune existing screening and detection models in the areas of onboarding, transaction monitoring and ongoing customer reviews. The expected outcome of these models is proactive identification and prevention of high risk prospects / customers and illicit activities, including new and emerging typologies
• Candidate is expected to build predictive models/analytics using:
  • a suite of statistical / data science techniques (regression, gradient boosted trees, network analytics, neural networks, NLP, audio/image processing, etc) that is best fit for purpose / use
  • both structured and unstructured data (test, audio, image, streams, etc) that can be found within the bank and external partners, vendors or in the internet in general (social media, web-crawled data, etc)
• Own and manage UOB’s business intelligence tool / dashboard (ie, Qliksense). This include managing the data cube and maintaining / creating new dashboards for users within Group Retail. You may also be required to provide reports or ad-hoc analyses using other means (eg, SAS)
• Support any data management / engineering activities related to AML – eg, procuring, ingesting/preparing data (including unstructured) from UOB’s datalake to datamarts/cubes, and ultimately for consumption in predictive models or dashboards
• Conduct / support any UAT or other testing of data, systems, tools related to AML/compliance
• Frequent and regular discussions with front-line teams (to understand new typologies, identify requirements, etc), group and business compliance teams, integrated fraud management teams, technology teams, etc to solution and implement assets / use cases to address AML, compliance and financial crime objectives

Job Requirements

• >5 years of experience in data science as applied to AML/fraud/financial crime
• Undergraduate degree in a quantitative programme, such as Statistics, Mathematics, Actuarial Science, Financial Engineering, etc. MBA / post graduate degree is an added advantage
• Very strong programming + technology skills:
  • SAS is a must for data management, ad-hoc analyses and modelling. Python is used for most newer/advanced algorithms and data science libraries
  • Cloudera Big Data Platform – for data engineering and data science work on UOB’s data lake
  • Qliksense – to build/enhance/maintain QVD cube and dashboards for business / non-technical users
  • MS Office macro programming for automation
• Analytical mind with sound business insight, excellent communicator (verbal and written), highly meticulous, and self-motivated
• Maturity that will enable the candidate to be a credible counterpart to business managers and senior management, and the ability to develop on-going ‘trusted advisor’ relationships based on the ability to understand, analyse, discuss and address key business challenges raised

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.

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