Job description

This is a rare opportunity to sit at the intersection of artificial intelligence and Finance transformation — not just supporting the strategy, but building it with your own hands. You'll architect the next generation of Artificial Intelligence-driven analytics and decision-support products for one of the world's leading asset and wealth management businesses. If you thrive on shipping products that stay ahead of the curve and solving the hardest data problems in the room, this role was designed for you. Join a team where your code, your models, and your ideas directly shape how Finance leaders make decisions. The impact here is tangible, visible, and yours to own. As a Vice President — Applied Artificial Intelligence and Data Product Scientist in the Finance Transformation and Analytics team within Asset and Wealth Management Finance, you will be an individual contributor who spends roughly 60% of your time hands-on building and 40% shaping strategy, roadmaps, and best practices. You will own two complementary mandates: first, researching, prototyping, and hardening next-generation AI capabilities so they are production-ready before the current product cycle completes; and second, diagnosing and fixing the structural data problems that undermine both AI and traditional reporting products. You will partner closely with Finance stakeholders, data engineering, and platform teams, but your primary value is building — writing code, training models, designing data architectures, and delivering working products that move measurably faster than the problems they solve. Job Responsibilities • Lead research and rapid prototyping of emerging AI capabilities — including agentic workflows, multi-agent orchestration, retrieval-augmented generation, and advanced forecasting — converting validated proofs of concept into production-grade products on a defined timeline • Maintain a rolling 6–12 month innovation pipeline by identifying, evaluating, and experimenting with new models, frameworks, and techniques so the team always has a next release in development • Architect, build, and scale large language model agents and AI-driven solutions for Finance workflows, integrating with APIs, microservices, reporting tools, and cloud platforms • Design evaluation harnesses, guardrails, and human-in-the-loop review processes for every AI product from prototype through production • Translate model outputs into user-friendly insights, analytics, and decision-support surfaces for Finance leadership and front office partners • Audit and remediate data quality and lineage gaps across reporting and analytics products so all downstream consumers work from a single source of truth • Define and implement a data catalogue strategy by inventorying existing assets, consolidating duplicative pipeline logic, standardizing metric definitions, and establishing clear data contracts • Architect curated and semantic data layers that are consistent, trusted, and reusable across reporting, dashboards, analytics, and AI products • Establish data observability practices including monitoring, alerting, and lineage tracking so issues surface before they reach end users • Define and own a forward-looking AI and data roadmap aligned to Finance priorities and the firm's broader technology direction • Design experimentation and measurement frameworks to quantify the business impact of AI products and data quality improvements, and communicate roadmap, trade-offs, and results to senior leaders through clear narratives and data-driven recommendations Required Qualifications, Capabilities, and Skills • 8 years of experience in data science, AI/ML, analytics, or product engineering, with hands-on delivery of production AI or ML applications • Hands-on experience with large language models including prompt engineering, retrieval-augmented generation, function and tool calling, embeddings, vector stores, and evaluation frameworks • Strong Python and SQL fluency with experience building and operating production data and inference pipelines end-to-end • Practical expertise with cloud data platforms such as AWS, Snowflake, or Databricks, including distributed processing and data modeling • Demonstrated ability to take AI or generative AI solutions from concept through prototype to scaled production • Experience diagnosing and fixing data quality, lineage, and governance problems in complex enterprise environments • Understanding of Finance functions such as financial planning and analysis, management reporting, forecasting, and key performance indicator frameworks within asset management, wealth management, or comparable financial services • Proficiency in applied statistics and experimentation design with the ability to measure business impact and communicate findings to non-technical audiences • Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, Finance, or a related quantitative field Preferred Qualifications, Capabilities, and Skills • Experience building multi-agent systems, autonomous workflows, or task planners using frameworks such as LangGraph, LangChain, or LlamaIndex • Hands-on experience building generative AI products with guardrails, evaluation harnesses, and human-in-the-loop review grounded in approved internal sources • Experience designing and governing semantic layers, metric stores, and data observability frameworks that support both business intelligence and AI consumption • Track record of designing and operationalizing experimentation programs including A/B testing and causal measurement to quantify product and data initiatives • Knowledge of model safety, bias mitigation, privacy techniques, and AI governance frameworks in financial services • Familiarity with modern business intelligence and visualization tools such as Tableau, Power BI, or ThoughtSpot • Prior experience in a Finance transformation or technology function supporting front office or investment management Shape the future of Finance with Artificial Intelligence — build next-gen products, fix data foundations, and drive measurable impact.

Requirements

Must-haves

Maintain a rolling 6–12 month innovation pipeline by identifying, evaluating, and experimenting with new models, frameworks, and techniques so the team always has a next release in development
Architect, build, and scale large language model agents and AI-driven solutions for Finance workflows, integrating with APIs, microservices, reporting tools, and cloud platforms
Design evaluation harnesses, guardrails, and human-in-the-loop review processes for every AI product from prototype through production
Translate model outputs into user-friendly insights, analytics, and decision-support surfaces for Finance leadership and front office partners
Audit and remediate data quality and lineage gaps across reporting and analytics products so all downstream consumers work from a single source of truth
Define and implement a data catalogue strategy by inventorying existing assets, consolidating duplicative pipeline logic, standardizing metric definitions, and establishing clear data contracts
Architect curated and semantic data layers that are consistent, trusted, and reusable across reporting, dashboards, analytics, and AI products
Establish data observability practices including monitoring, alerting, and lineage tracking so issues surface before they reach end users
Define and own a forward-looking AI and data roadmap aligned to Finance priorities and the firm's broader technology direction
Required Qualifications, Capabilities, and Skills
8 years of experience in data science, AI/ML, analytics, or product engineering, with hands-on delivery of production AI or ML applications
Hands-on experience with large language models including prompt engineering, retrieval-augmented generation, function and tool calling, embeddings, vector stores, and evaluation frameworks
Strong Python and SQL fluency with experience building and operating production data and inference pipelines end-to-end
Practical expertise with cloud data platforms such as AWS, Snowflake, or Databricks, including distributed processing and data modeling
Demonstrated ability to take AI or generative AI solutions from concept through prototype to scaled production
Experience diagnosing and fixing data quality, lineage, and governance problems in complex enterprise environments
Proficiency in applied statistics and experimentation design with the ability to measure business impact and communicate findings to non-technical audiences
Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, Finance, or a related quantitative field
Experience building multi-agent systems, autonomous workflows, or task planners using frameworks such as LangGraph, LangChain, or LlamaIndex
Hands-on experience building generative AI products with guardrails, evaluation harnesses, and human-in-the-loop review grounded in approved internal sources
Experience designing and governing semantic layers, metric stores, and data observability frameworks that support both business intelligence and AI consumption
Track record of designing and operationalizing experimentation programs including A/B testing and causal measurement to quantify product and data initiatives
Knowledge of model safety, bias mitigation, privacy techniques, and AI governance frameworks in financial services

Nice-to-haves

Preferred Qualifications, Capabilities, and Skills

Application prep

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Keywords to mirror

aimlpythonsqlaws

Platform: oracle-cloud
Effort: medium