This role requires a unique combination of deep technical expertise, strategic thinking, and collaborative leadership to make data available for AI/analytics, provide transparency into data flows, embed preventative controls, and enrich metadata to accelerate adoption.
As an Applied AI ML Lead in Asset and Wealth Management-Strategic Data Provisioning (SDP) team , you will be is responsible for accelerating AWM's data and analytics journey. The team plays a critical role in modeling behaviors to drive adoption, manage dependencies, align resources, foster innovation, and demonstrate value across the data lifecycle.
Job Responsibilities
- Make data available for AI and analytics initiatives, working closely with use case owners to define requirements and manage product dependencies
- Provide transparency and visibility into bottlenecks and progress in making AI-ready data available for innovation.
Collaborate with business, technology, and operations partners to understand data requests and accelerate provisioning through deployment of "AI for Data"
- Drive executive visibility of progress in making critical data sources available, including performance metrics and adoption tracking . Support agile product routines to oversee cross-product data dependencies and prioritize delivery
- Identify the lineage and provenance of critical data assets to support governance, regulatory, and business requirements .
Embed evergreen controls on data flows to improve safety and meet regulatory requirements
- Develop and deliver data lineage analysis and documentation that provides executive visibility on progress meeting critical SLAs (including blockers, resourcing, etc.)
- Uplift data flows for critical data to include controls, transparency, and traceability .
Drive insight into areas of efficiency and risk through consolidation and reengineering of data flows
- Lead data quality issue root cause analysis using deep data profiling and advanced analytics techniques .
Fix the cause of identified data quality issues and embed uplifted evergreen controls on data flows to prevent future failures
- Develop proactive controls to reduce the time from data quality issue identification to resolution, improving client experience .
Drive operational efficiency through elimination of cost of poor quality (COPQ) .
Demonstrate control environment improvements and reduction in toil to achieve benefits through common tooling and frameworks
- Uplift the metadata (semantic layer) of existing data to make it more valuable to users and AI applications (AKA "Brownfield" data enrichment) .
Support AI and Natural Language Query (NLQ) usage through enhanced data cataloging and discoverability
- Accelerate adoption of Mesh data architecture by enriching existing data assets with improved metadata, data quality scores, and lineage information
- Reduce consumer friction due to poor data catalog quality and incomplete documentation .
Develop and deliver data product prototypes that demonstrate the value of uplifted data assets
Required Qualifications, Capabilities, and Skills
- 10+ years of experience in data science, analytics, data engineering, or data management within financial services
- Deep subject matter expertise in wealth and asset management, covering customer, account, position, transaction, and/or reference data domains
- Proven execution ability in a matrixed and complex environment with the ability to influence people at all levels of the organization
- Experience in strategic or transformational change initiatives, including data governance, data quality, or analytics transformation programs
- Experience in leading data teams and delivering on applied AI initiatives
- Strong technical skills in data profiling, analysis, and data management using modern tools and environments (Python, R, SQL, Spark, cloud platforms)
- Understanding of data lineage concepts and experience with lineage analysis, metadata management, and data cataloging
- Excellent communication skills with the ability to convey complex technical concepts to diverse audiences including executive leadership
- Ability to work in a highly collaborative and intellectually challenging environment
- Willingness to challenge the status quo, think creatively, problem-solve, and drive innovation
- Experience with data quality frameworks, including profiling, rule development, issue remediation, and preventative controls
Preferred Qualifications, Capabilities, and Skills
- Strong proficiency in data science and analytics tools: Python, R, SQL, Spark, and cloud data platforms (AWS, Azure, GCP)
- Experience with data visualization and reporting tools (e.g., Tableau, Power BI) to deliver executive dashboards and performance metrics
- Hands-on experience with data lineage tools and techniques, including graph databases and metadata management platforms
- Knowledge of data governance frameworks, data quality dimensions, and regulatory requirements (e.g., BCBS 239, GDPR)
- Experience with AI/ML technologies and their application to data management challenges (e.g., automated data profiling, metadata enrichment)