VP, Data Product Management

Sumitomo Mitsui Banking Corporation•Charlotte, NC

3d•Hybrid

About The Position

SMBC Group is a top-tier global financial group with a 400-year history, headquartered in Tokyo. It offers diverse financial services and has a significant international presence with over 130 offices and 80,000 employees worldwide. In the Americas, SMBC Group provides commercial and investment banking services to corporate, institutional, and municipal clients, leveraging its global network. The Group's operating companies in the Americas include Sumitomo Mitsui Banking Corp. (SMBC), SMBC Nikko Securities America, Inc., and others. This role is for a hands-on Business Data Analyst within the Data Product Management team, partnering with Finance, Risk, and Technology. The primary focus is on delivering capital stress testing risk models and financial reports for internal and regulatory initiatives, including CCAR.

Requirements

5+ years of total experience combining Business Analysis and hands-on software development experience within financial services or financial technology.
Strong data analysis skills, including hands-on experience with SQL and Python.
Hands-on experience with ML/AI development tools (such as Azure ML, AWS SageMaker, PyTorch) and cloud computing platforms (AWS, Azure, Databricks, Snowflake).
Demonstrated hands-on transformation experience: you have directly helped design, build, or implement process and tooling changes (beyond coordination/PMO activities).
Self-driven, self-motivated, and able to operate independently while collaborating effectively in a squad/team environment.
Capable of understanding and solving highly complex problems.
Excellent communication skills.
Experience in financial services, preferably in the areas of commercial banking, investment banking and trading.
Solid understanding of Capital Markets, preferably in Sales & Trading or Investment Banking.
Experience working with trade data, position data, and reference data.
Experience working in financial services with exposure to regulatory reporting and/or CCAR (or closely related stress testing processes).
Excellent written and verbal communication skills, with the ability to engage senior stakeholders.

Responsibilities

Design and transform manual processes using AI and digital tooling (e.g., CoPilot, Power Automate, Power BI, Alteryx, and emerging AI-enabled tools).
Develop and influence deliverables directly: create prototypes, analytics, dashboards, and automations that accelerate decision-making and reduce manual work.
Serve as business analyst for Data transformation, Risk Modeling and Financial Reporting initiatives, translating model development, risk reporting, financial reporting and regulatory requirements (e.g., CCAR) into clear user stories, process maps, and functional requirements.
Support production processes for systems and workflows used in CCAR, including triage of issues, root-cause analysis, and coordination of fixes with Technology and business owners.
Partner with Capital Planning SMEs, Technology, and key Risk and Finance stakeholders to identify pain points, define target-state workflows, and deliver solutions that improve controls, transparency, and efficiency.
Serve as the squad’s Scrum Master: facilitate Agile ceremonies (stand-ups, refinement, planning, retrospectives), remove blockers, and help the team continuously improve delivery practices and velocity.
Prepare documentation, training materials, and test artifacts (test scripts, scenarios, acceptance criteria) to enable consistent adoption across stakeholder groups.
Establish frameworks for data validation, integration testing, and performance monitoring.
Ensure platform reliability and data integrity through collaboration with QA and engineering teams for continuous build and delivery (CI/CD) of features.
Plan and facilitate user acceptance testing and stakeholder sign-offs; coordinate release readiness and communications.
Maintain working knowledge of the complete technology stack supporting Model training, including cloud computing, Machine Learning frameworks, and orchestration systems.