Applied AI & Data Product Scientist
About The Position
As a Vice President and individual contributor on the Finance Transformation and Analytics team within Asset and Wealth Management Finance, this role combines hands-on product development with strategy, roadmaps, and best practices. Approximately 60% of the work is hands-on building and 40% is shaping strategy. The role has two complementary mandates: researching, prototyping, and hardening next-generation AI capabilities for production, and diagnosing and resolving structural data problems that affect AI and traditional reporting products. The scientist partners with Finance stakeholders, data engineering, and platform teams, and builds code, trains models, designs data architectures, and delivers working products. The role develops AI-driven analytics and decision-support products for Finance, including emerging capabilities such as agentic workflows, multi-agent orchestration, retrieval-augmented generation, and advanced forecasting. It also improves data quality, lineage, cataloguing, semantic layers, and observability across reporting and analytics. The position owns an AI and data roadmap aligned with Finance priorities and the firm’s technology direction, and measures product and data-improvement outcomes for senior leaders.
Requirements
- 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.
Nice To Haves
- 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 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.
Responsibilities
- Lead research and rapid prototyping of emerging AI capabilities, 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.
- Architect, build, and scale large language model agents and AI 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 AI products 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.
- Define and implement a data catalogue strategy, including asset inventory, consolidation of duplicative pipeline logic, standardized metric definitions, and 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.
- Define and own an 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.
- Communicate roadmap, trade-offs, and results to senior leaders through clear narratives and data-driven recommendations.
Benefits
- Competitive total rewards package, with base salary determined by role, experience, skill set, and location.
- Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
- Comprehensive health care coverage.
- On-site health and wellness centers.
- Retirement savings plan.
- Backup childcare.
- Tuition reimbursement.
- Mental health support.
- Financial coaching.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
