Roles and Responsibilities
– Work closely with senior product managers and product owners to define the product vision, roadmap, and requirements for AI Industrialization Tooling.
-Capture the requirements as user stories with clearly defined success metrics and acceptance criteria.
-Participate in key agile rituals (e.g., daily stand-up, backlog refinement, sprint planning, sprint review, etc.,) and work closely with ML engineers and product developers to ensure that requirements are clearly understood by the developers. Track the product development progress and identify any potential risk and issues.
-Lead and conduct product UAT
-Clearly define UAT test scenarios and acceptance criteria
-Conduct test cases and document the test results with identified errors/issues
-Lead the communication with ML engineers and product developers to ensure the errors/issues identified during UAT are fixed before the release the product.
-Collaborate with customer success team to develop product training materials
-Engage the analytics community and key stakeholders from different BU/SUs to understand their requirements and priorities to ensure the product delivered under AI Industrialization meet the real needs.
-Engage the analytics community within the bank to promote the products and drive for higher adoption and user satisfaction.

Skills and Qualifications
-At least Bachelor’s degree in in computer science, business analytics, statistics, or related fields
-Proficient with Python/Pyspark and SQL
-Demonstrated experience of applying data science and machine learning methods to real-world data problems
-Familiar with both AI/ML Lifecyle (e.g., CRISP-DM) and Analytics software development lifecycle
-Familiar with popular data science workbench and tools (e.g, Cloudera Data Science Workbench, Amazon SageMaker, DataRobot, Dataiku, etc.)
-Highly effective cross-functional team communication and collaboration
-Exceptional writing skills combined with strong presentation and public speaking skills.

Preferred Qualifications
-Master’s degree in computer science, business analytics, statistics, or related fields
-2 to 5 years of professional experience
-Proven experience working as a Product Manager/ML Engineer/Data Scientist
-Previous software and application development experience.