Kuala Lumpur NBE, Malaysia
Hybrid
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.
Job Description
Support model maintenance
Liaise with key stakeholders/end users to receive and understand business feedback on model performance and incorporate feedback into models.
Re-train and recalibrate existing AFC analytical models to prevent model drift periodically or as needed.
New model development
Work closely with model end-users and other key stakeholders (e.g., Data and Ops Engineer, Group Compliance and Wholesale/Retail business sectors, etc.) to identify additional areas which require analytics support or future model build and include those models in development pipeline.
Develop model narratives (e.g., purpose, logic, parameters, data requirements, output surfacing/structuring, etc.) in collaboration with the business and other relevant stakeholders.
Work closely with other Data Scientist(s) and undertake the end-to-end AFC model development, including data wrangling, exploratory data analysis, feature selection, model selection, training, testing, etc.
Build a range of models (e.g., rule-based, supervised/unsupervised models, etc.) on structured, semi-structured, and/or unstructured data if needed.
Special projects
Support special/high-priority projects, including building AFC analytical capabilities in response to external (e.g., regulatory, etc.) requests or internal intelligence, or based on gaps identified for existing analytical capabilities/models.
Keep abreast with the latest, cutting-edge developments in data science and advanced analytics and recommend adoption of best practices within the bank, in relation to AFC/AML.
Understand new regulatory requirements/policy updates relating to the areas/risks covered by the AFC analytical models and work with the relevant teams to ensure that these updates are appropriately reflected in those models.
Job Requirements:
5 – 10 years of experience working in the technology space and preferably more than 4 years of data science/data analytics experience.
Minimum 3 years of experience working with advanced analytical models/tools/applications (e.g., machine learning, etc.).
Prior experience working on large-scale analytics projects.
Experience in or familiarity with analytics related to AML/AFC/compliance risks.
Ability to clearly communicate technical results in an easy-to-understand manner and tailoring them to different audiences.
Ability to handle multiple priorities and work under pressure.
Bachelor’s degree, or equivalent, in Computer Science, Engineering, Statistics, Mathematics, Business Analytics etc.
Technical skills Required:
R and Python for data science, with practical knowledge of data wrangling and machine learning libraries (e.g., Pandas, Keras, Tensorflow, Sklearn, etc.).
Comfortable working with structured and unstructured data and distributed databases.
Familiar with natural language processing and network link analysis.
Familiar with best practice development standards.
Additional Requirements
Develop, Engage, Execute, StrategiseBe a Part of the 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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