Applied AI ML Lead- Data Scientist Specialist

Job Description

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.

• 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.

• 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 .

• 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 .

• Lead data quality issue root cause analysis using deep data profiling and advanced analytics techniques .

• 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) .

• Uplift the metadata (semantic layer) of existing data to make it more valuable to users and AI applications (AKA Brownfield data enrichment) .

• 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

• 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

• 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)