Applied AI & Data Product Scientist
Salary not listed • Jersey City, NJ, United States • Full-time
Posted 14d ago
No salary is listed. Many US states require employers to post a pay range, so ask about pay early.
Job Location
Jersey City, NJ, United States
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Engineer
About the job
This role offers a unique opportunity to be at the forefront of artificial intelligence and Finance transformation, not just by supporting strategy but by actively building it. You will architect the next generation of AI-driven analytics and decision-support products for a leading asset and wealth management business. The position is ideal for someone who excels at shipping cutting-edge products and solving complex data challenges. You will join a team where your contributions directly influence how Finance leaders make critical decisions, with tangible and visible impact.
As a Vice President — Applied Artificial Intelligence and Data Product Scientist, you will be an individual contributor focusing approximately 60% on hands-on building and 40% on strategy, roadmaps, and best practices. Your responsibilities will include researching, prototyping, and productionizing next-generation AI capabilities, as well as diagnosing and resolving structural data issues that affect both AI and traditional reporting products. You will collaborate with Finance stakeholders, data engineering, and platform teams, with your primary value derived from building code, training models, designing data architectures, and delivering functional products.
Responsibilities
- Lead research and rapid prototyping of emerging AI capabilities, including agentic workflows, multi-agent orchestration, RAG, and advanced forecasting, and convert them into production-grade products.
- Maintain an innovation pipeline by identifying, evaluating, and experimenting with new models, frameworks, and techniques.
- Architect, build, and scale LLM 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 AI products.
- Translate model outputs into user-friendly insights, analytics, and decision-support surfaces for Finance leadership.
- Audit and remediate data quality and lineage gaps across reporting and analytics products.
- Define and implement a data catalog strategy by inventorying assets, consolidating logic, standardizing metrics, and establishing data contracts.
- Architect curated and semantic data layers that are consistent, trusted, and reusable across various products.
- Establish data observability practices including monitoring, alerting, and lineage tracking.
- Define and own a forward-looking AI and data roadmap aligned with Finance priorities.
- Design experimentation and measurement frameworks to quantify the business impact of AI products and data quality improvements, and communicate results to senior leaders.
Requirements
- 8 years of experience in data science, AI/ML, analytics, or product engineering with hands-on delivery of production AI/ML applications.
- Hands-on experience with LLMs, including prompt engineering, RAG, function/tool calling, embeddings, vector stores, and evaluation frameworks.
- Strong Python and SQL fluency with experience building and operating end-to-end production data and inference pipelines.
- Practical expertise with cloud data platforms (e.g., AWS, Snowflake, 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 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.