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 experiencein data science, analytics, data engineering, or data management within financial services
Deep subject matter expertisein wealth and asset management, covering customer, account, position, transaction, and/or reference data domains
Proven execution abilityin 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 teamsand delivering on applied AI initiatives
Strong technical skillsin data profiling, analysis, and data management using modern tools and environments (Python, R, SQL, Spark, cloud platforms)
Understanding of data lineage conceptsand experience with lineage analysis, metadata management, and data cataloging
Excellent communication skillswith the ability to convey complex technical concepts to diverse audiences including executive leadership
Ability to work in ahighly collaborative and intellectually challenging environment
Willingness to challenge the status quo, think creatively, problem-solve, and drive innovation
Experience withdata quality frameworks, including profiling, rule development, issue remediation, and preventative controls

Preferred Qualifications, Capabilities, and Skills

Strong proficiency indata science and analytics tools: Python, R, SQL, Spark, and cloud data platforms (AWS, Azure, GCP)
Experience withdata visualization and reporting tools(e.g., Tableau, Power BI) to deliver executive dashboards and performance metrics
Hands-on experience withdata lineage toolsand techniques, including graph databases and metadata management platforms
Knowledge ofdata governance frameworks, data quality dimensions, and regulatory requirements (e.g., BCBS 239, GDPR)
Experience withAI/ML technologiesand their application to data management challenges (e.g., automated data profiling, metadata enrichment)

Applied AI ML Lead- Data Scientist Specialist