Senior Product Manager, AI Data Fabric & ML

Advisor360•Needham, MA

2d•$175,000 - $225,000•Hybrid

About The Position

In this role you will be the product owner for Advisor360°'s data fabric, the trust infrastructure that connects household relationships, account structures, and advisor context across the platform. Your job is to deepen that foundation: strengthening governance, quality, and product discipline as AI capabilities scale. Own the product strategy and roadmap for the data fabric's evolution, including data product contracts, lineage, metadata management, and quality observability Define what a "certified data product" means at Advisor360° and build the lifecycle to support it from registration through retirement Extend the fabric's governance model so domain teams can own their source-of-truth systems while the Data Fabric team provides shared standards, tooling, and quality frameworks Shape canonical data models, enriched entities, and AI-ready representations that ML and GenAI systems consume, building on the existing household and relationship models Champion developer experience across the data platform: APIs, MCPs, documentation, onboarding, and self-service access Partner with Compliance and Legal to ensure data governance meets the auditability, explainability, and regulatory standards required in wealth management Own vendor relationships and integration strategy for metadata management and data observability tooling Data Science Operations Product teams across Advisor360° build AI-powered features, and those features depend on Data Science and ML models. The engineers who build and maintain those models sit on your team. You manage that supply side. Manage prioritization and capacity for DS/ML engineering work across product teams, ensuring the team builds, retrains, and maintains the right models at the right time Own the operational health of DS/ML models in production, monitoring for drift, data quality degradation, and performance changes that product PMs may not have visibility into Partner with product PMs to translate their feature requirements into DS/ML engineering work; they define what "good" means for their users, and you ensure the models underneath reliably deliver on their intended outcomes Drive adoption of shared observability, validation, and monitoring tooling that gives product teams and leadership visibility into model behavior Provide clear reporting on DS/ML capacity, operational risk, and model health for Product, Engineering, and Executive leadership Product PMs own quality and release decisions for their features. Your job is to make sure the DS/ML dependencies they rely on stay well-built, well-monitored, and never quietly degrade beneath them. Cross-Functional Leadership Act as the single product interface for the Data Fabric team, coordinating across Data Science, AI Engineering, Platform Engineering, and domain product teams Drive alignment on prioritization and sequencing across teams that have historically operated independently Guide and mentor other Product Managers on data-informed practices, evaluation thinking, and working with technical platform teams

Requirements

7+ years of product management experience, with meaningful time on data platforms, data governance, developer tooling, or infrastructure products, ideally including experience scaling an existing platform through a major capability evolution
Experience driving adoption of platform capabilities across teams that did not ask for them. You know that the hardest part of a data platform is not building it; it is getting people to use it
Strong enough technically to hold your own in architectural discussions about data modeling, lineage, pipeline design, and system dependencies. You’re a builder that knows when something is wrong
Working familiarity with the data and AI landscape: you understand how ML, LLMs, RAG, and agentic systems consume data, even if you have not built them
Experience defining quality criteria and evaluation frameworks for data or ML systems
Highly analytical, with the ability to bring structure to ambiguous, high-stakes problems where the right answer is not obvious
Proven ability to influence senior leadership and align teams around complex, cross-cutting initiatives without positional authority
Clear, precise communicator who can translate between engineering depth and executive context
Data-fluent: comfortable with SQL, BI tooling, and using quantitative evidence to make product decisions

Nice To Haves

Experience in financial services, wealth management, or another highly regulated industry (fintech, healthcare, legal tech) where compliance and auditability serve as design constraints, not afterthoughts
Experience standing up or operating a data governance function: data catalogs, data contracts, metadata management, data quality programs
Familiarity with the modern data stack (Snowflake, dbt, and comparable tooling) and data observability platforms
Experience working with federated or domain-oriented data ownership models

Responsibilities

Own the product strategy and roadmap for the data fabric's evolution, including data product contracts, lineage, metadata management, and quality observability
Define what a "certified data product" means at Advisor360° and build the lifecycle to support it from registration through retirement
Extend the fabric's governance model so domain teams can own their source-of-truth systems while the Data Fabric team provides shared standards, tooling, and quality frameworks
Shape canonical data models, enriched entities, and AI-ready representations that ML and GenAI systems consume, building on the existing household and relationship models
Champion developer experience across the data platform: APIs, MCPs, documentation, onboarding, and self-service access
Partner with Compliance and Legal to ensure data governance meets the auditability, explainability, and regulatory standards required in wealth management
Own vendor relationships and integration strategy for metadata management and data observability tooling
Manage prioritization and capacity for DS/ML engineering work across product teams, ensuring the team builds, retrains, and maintains the right models at the right time
Own the operational health of DS/ML models in production, monitoring for drift, data quality degradation, and performance changes that product PMs may not have visibility into
Partner with product PMs to translate their feature requirements into DS/ML engineering work; they define what "good" means for their users, and you ensure the models underneath reliably deliver on their intended outcomes
Drive adoption of shared observability, validation, and monitoring tooling that gives product teams and leadership visibility into model behavior
Provide clear reporting on DS/ML capacity, operational risk, and model health for Product, Engineering, and Executive leadership
Act as the single product interface for the Data Fabric team, coordinating across Data Science, AI Engineering, Platform Engineering, and domain product teams
Drive alignment on prioritization and sequencing across teams that have historically operated independently
Guide and mentor other Product Managers on data-informed practices, evaluation thinking, and working with technical platform teams