Associate Director, Data Science Job Description

Its responsibilities entail:
•    Responsible for creation, maintenance, and successful execution of a machine learning operation & delivery (MLOps) ecosystem for the continuous delivery of AI artifacts (models, analyses, and predictions) developed by the SCMAC BA Coe, to WRB markets and to WRB digital solutions.
•    Responsible for developing and maintaining robust data workflows and pipelines using python and pyspark to process and analyse large-scale datasets to adopt new data feeds and feature stores, and in particular – incorporating our WRB unified data model (Athena) to reduce latency and improve the quality of data artifacts.
•    Experience in project ownership – end-to-end accountability for the project lifecycle, steering cross-functional teams and making critical decisions for delivering state-of-art WRB AI Solutions while ensuring alignment with organizational goals.
•    Collaboration with a wide range of stakeholders including data scientists (SCMAC BA Coe), business analysts, governance, architect and software engineers (Enterprise Technology, CDO and WRB CIO Data Engineering teams) on the delivery of a fit-for-purpose big data ecosystem for building AI (ML/Gen AI) solutions in SCMAC BA Coe.
•    Support the continuous improvement of the tools, technology, and data ecosystem available for all data roles in the SCMAC BA Coe.
•    Implement best practices for version control, testing and CI/CD in ML pipelines
•    Stay updated on emerging technologies and trends in MLOps, advanced AI (Gen AI) and cloud-based data and analytics platforms (Databricks, Dataiku).
•    Ensure solutions adhere to SCB’s model, risk, compliance and responsible AI standards.

Key Responsibilities

Strategy
The individual will be part of the AAIOps team, responsible for shaping a state-of-the-art ecosystem (in terms of tools, technology and datamart) for data, analytics & AI roles within SCMAC while aligning with wider SCB tech simplification effort. The individual will play a crucial part in operationalizing AI/ML models or other analytical pipeline at scale, ensuring efficient and reliable deployment across diverse environments. 

Business
•    Responsible for creation, maintenance, and successful execution of a machine learning operation & delivery (MLOps) ecosystem for the continuous delivery of AI artifacts (models, analyses, and predictions) developed by the SCMAC BA Coe, to WRB markets and to WRB digital solutions.
•    Collaboration with both data scientists (SCMAC BA Coe) and software engineers (Enterprise Technology& WRB CIO Data Engineering teams) on the delivery of a fit-for-purpose big data ecosystem for building AI solutions in SCMAC BA Coe, transitioning from proprietary technology (SAS) to open-source technologies.
•    Support the continuous improvement of the tools, technology, and data ecosystem available for all data roles in the SCMAC BA Coe.
•    Implement best practices for version control, testing and CI/CD in ML pipelines
•    Stay updated on emerging technologies and trends in MLOps, advanced AI (Gen AI) and cloud-based data and analytics platforms (Databricks, Dataiku).
•    Responsible for developing robust data workflows and pipelines using python and pyspark to process and analyse large-scale datasets to adopt new data feeds and feature stores, and in particular – incorporating our WRB unified data model (Athena) to reduce latency and improve the quality of data artifacts.

Processes
•    Continuously improve the operational efficiency and effectiveness of processes
•    Ensure effective management of operational risks within the function and compliance with applicable internal policies, and external laws and regulations

People & Talent 
•    Collaborate with the team members on their work for better quality and accuracy 
•    Be able to work in agile fashion with other teams and support development /implementation of the solution
•    Contribute towards training sessions for knowledge management
•    Driving an environment of collaboration, both within CPBB Banking and across the wider Group, to ensure issues are raised and blockages are resolved in a timely manner 

Risk Management 
•    Interpret the Group’s financial information, identify key issues based on