Lead Product Manager, AI & Data Intelligence
- Compensation
- $190K–$230K / yr
- Location
- Palo Alto Office (Hybrid)
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 7+ years
- Category
- Product
- Posted
Skills
Must have
- You have shipped real AI products, not just used AI tools. You have taken LLM-powered or model-driven features from prototype to production, lived the gap between a great demo and a reliable product,
- You’ve lived this industry. 7+ years in product in fintech, ideally across investment data products in wealth management, asset management, or private markets. You know what RIAs, fund managers, and t
- You operate with extreme ownership. If the data is wrong, you fix it at the source. If the model output is wrong, you find out before the client does. Nobody has to ask, and nothing is someone else’s
- You are obsessive about correctness. A single wrong cash flow, misstated return, or confidently wrong AI insight breaks trust with an advisor. You build the systems and standards that prevent it, and
- You live on client calls and treat Data Operations and client-facing teams like partners, not a ticket queue. You find the real problem and build the thing that actually solves it, rigorous enough for
Full description
ABOUT ALLOCATE
Allocate is building the intelligent private markets operating system for the wealth channel. We give RIAs, family offices, and institutional allocators modern infrastructure for discovering, accessing, and managing private market investments. Today that means 350+ wealth advisory firms, 1,500+ GP relationships, and over $5 billion in platform assets, and we are scaling fast.
ABOUT THE ROLE
You will own the intelligence layer of Allocate: the AI systems that turn raw private markets documents and data into insight clients trust and act on.
The raw material is some of the messiest data in finance: capital account statements, fund documents, cash flow notices, unstructured PDFs in a thousand formats. Underneath your product sits a data and extraction pipeline, built in close partnership with our Data Operations team, that turns those inputs into a structured, reliable data layer. You own that pipeline as a product, and your mandate is to make it AI-native end to end: extraction, structuring, validation, and quality handled by intelligence, not brute-force human effort.
But the pipeline is the substrate, not the product. The product is what the intelligence does with it: surfacing patterns in investment performance and outcomes that no advisor would find by reading raw numbers, powering our Insights and Diligence products, and answering the question every end investor actually has, which is "how is my portfolio really doing, and what should I pay attention to?" You will decide where models create real leverage, design the systems that keep their output accurate enough to put in front of paying clients, and ship intelligence that is measurably right, not just impressive in a demo.
That last part is the hard part, and it is why this role exists. Anyone can wire an LLM to a database. Making AI-generated analysis reliable enough that an RIA will present it to their client requires evals, monitoring, quality gates, and product judgment about when the system is good enough to ship and when it is not. We hold AI features to hard eval gates before they reach clients. You will own those gates.
This is deliberately a wide seat: the pipeline, the client-facing data experience, and the intelligence on top. We keep them together because splitting them produces exactly the disconnected, half-trusted data products this industry is full of. If that scope reads as too much, this is not your role. If it reads as the whole point, keep going.
WHY THIS ROLE MATTERS
In private markets, data is the product. When an advisor opens a client’s portfolio, the completeness of the look-through data, the accuracy of the cash flows, and the quality of the analytics are the product experience. When the end investor asks how their portfolio is really doing, the answer is only as good as the intelligence built on top of the data.