Manager, Data Analyst and Insights

Job responsibilities:

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 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.

Support model development and deployment

• Liaise closely with relevant stakeholders (e.g., Data Management Office) to identify and assess suitability of data for AFC models.
• Liaise with Business Analyst and ensure assembled data sets used for model build meet business requirements.
• Support Data Scientist(s) in developing model narratives by providing inputs from a data perspective (e.g., data requirements, data availability)
• Support Data Scientist(s) 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 bank 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 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 bank in adopting and/or migrating to new production environment, if needed

Team Contributions

• Support the validation team in defining and maintaining minimum standards of model performance, applicable at the time of model development and upheld via subsequent validation exercises
• Keep up to date with changes to regulatory requirements, recommendations and industry standards in analytics and management of AML / AFC risk, and propose updates to minimum standards of model development (in discussion with validation team lead)
• Support the developing validation calendar
• Where required provide views on tools / system enhancements required to conduct model validation.

Model Validation

• Conduct independent validation of AFC / AML models as per the standards defined by the validation team, and document all findings in a comprehensive report, along with recommended areas of attention (to bridge any gaps identified)
• Review further action by model owners on areas of attention identified in previous reports related to the same model, and call out gaps (if any)
• Check and challenge all steps of the model creation, data usage and value to the business. Review all codes used in model development and implementation for accuracy and consistency with documented approach.
• Where needed, produce and present reports to various working teams on validation findings.
• Prepare independent reports on performance of AFC models to senior management on a regular basis
• Remain involved in the review panel for any new models developed by the Modelling team / other teams when delegated by validation lead.

Job requirements:

• Bachelor's degree, or equivalent, Computer Science, Engineering, IT, Statistics, Mathematics, Business Analytics etc.
• The role is expected to have 5 - 7 years of experience working in technology and/or data analytics space, preferably 2 - 3 years of experience working in with advanced analytical models/tools/applications (e.g., machine learning, data-lake) in banking areas.
• Comfortable working with structured and unstructured data and distributed databases. Familiar with best practice development and validation standards
• Basic understanding and knowledge of banking, risk management, regulatory recommendations and industry standards related to AML/ AFC/ compliance risks. Experience in or familiarity with analytics related to AML/AFC/Compliance risks are advantages.
• Ability to clearly communicate complex results in an easy-to-understand manner and tailoring them to different audiences.
• Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
• Experience in data engineering tools, schema design, dimensional data modelling, robotic process, natural language processing and network link analysis
• Strong communication skills to interact with data scientists, business end-users, and possibly external vendors to design and develop data solutions.