Applied AI ML Lead- Data Scientist Specialist

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)