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
- Cover Data Analytics (DA) development and support
- Liaise closely with relevant stakeholders to identify and assess suitability of data for AFC models, and ensure assembled data sets used for model build meets business requirements
- Design and implement AFC models and 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 a 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., Local/Group Technology and Operations) to ensure models are production ready
- Ensure the seamless deployment of new AFC and fraud analytics solutions and models without breaking anything or creating unintended effects in the production pipeline
- Develop and maintain analytics solutions to support complex investigations, thematic reviews, improve the effectiveness of risk management and promote productivity gain.
- Performs data analysis required to tune up AFC system's parameter.
Job Requirements
- At least 8 years of experience working in a machine learning, data science, data engineering and/or data operations role, ideally in the financial services industry. Experience in fraud analytics or risk management preferred
- Good Bachelor's degree in Computer/Data Science, Computer Engineering, IT, Statistics or equivalent
- Experience in deploying and scaling AI/ML models
- Proficiency in data analysis tools and software (SQL, R, Python, or SAS) to code complex data engineering use cases
- Experience with big data analytics tools and frameworks 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
- Excellent analytical, problem-solving, and decision-making skills
- 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
- Able to instill strong Model Governance throughout the model development cycle
: Remark: The Bank requires the checking and collection of criminal records for candidates of this position in order to verify qualifications and/or disqualifications for the job position in accordance with the Bank's policy.
Additional Requirements
Develop, Engage, Execute, Strategise
Be 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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