Lead Product Manager, AI & Data Intelligence
Allocate · Palo Alto · $190,000–$230,000 (stated in the posting)
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.
This role decides whether that answer is trustworthy and illuminating or incomplete and flat. Your impact shows up in how much manual extraction work intelligence eliminates, how confidently an RIA puts our numbers and our AI-generated analysis in front of their clients, and how often the system surfaces something about performance or outcomes the user would never have found on their own.
WHAT YOU’LL OWN
The AI intelligence layer. The models, agents, and systems that generate insights about investment performance and outcomes across Insights, Diligence, and other data-driven products. You decide what gets built, where AI creates leverage versus noise, and what ships.
Evals and quality gates. AI output that reaches clients passes hard, automated evaluation first. You define what "correct" means for each feature, build the eval and monitoring systems that enforce it, and hold the line when something is not ready. Stale, wrong, or hallucinated output in front of a client is a product failure you own.
The AI-native data pipeline. In close partnership with Data Operations, the extraction and structuring services that turn raw documents into reliable inputs, with intelligence progressively replacing manual work. Their firsthand knowledge of what breaks is your best product input.
The client-facing data experience. How advisors, operations teams, investment teams, and end investors access and understand portfolio data: look-through investments, cash flows, performance, and cash flow modeling. Sophisticated data made clear and intuitive, down to the column order, the tooltip, and every word of copy.
Data quality as a product. Completeness, accuracy, and reliability are measured, improved, and held to a standard. These numbers go in front of clients; there is no tolerance for incorrect data, period.
HOW YOU’LL BE MEASURED
This seat comes with a scoreboard, and you will want one.
Within 90 days, you own the quality metrics we stand behind with clients: eval pass rates on AI-generated output, data completeness and accuracy, and monitoring that catches problems before clients do.
Within six months, you have shipped a measurable step-change in either extraction leverage (intelligence replacing manual work per document) or client-facing insight (AI-generated analysis clients act on), with the eval evidence to prove it works.
Ongoing, you present outcomes directly to the CEO. Not status updates. Outcomes: what shipped, what moved, what you’re killing and why.
WHO YOU ARE
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, and built or owned the evals that closed it. You have opinions about hallucination rates, grounding, and when an agentic approach beats a deterministic one, because you have paid for the wrong answer before.
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 their investors expect: the data, the conventions, the standards.
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 job.
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 will not rest until the experience is right, down to the last tooltip and word of copy.
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 an investment team and intuitive enough for an end investor. You prototype with modern AI tools daily and raise that standard for everyone around you.
THIS ROLE IS NOT FOR YOU IF
Your AI experience is adding a chat window to an existing product. This role is about making intelligence provably correct, which is much harder.
You want requirements handed to you fully specified. Here, you create structure where none exists.
You need consensus before acting. You will make calls with incomplete information and own the outcome.
You are optimizing for a slower pace right now. This is one of the highest-intensity seats in the company, and one that will help define the future of Allocate.
DETAILS
Reports to: Head of Product, working directly with the CEO on data and intelligence strategy.
Location: Palo Alto, CA.
Compensation: Base salary of $190K to $230K plus discretionary bonus, and equity.
Benefits: Medical, dental, vision, responsible time off (RTO), 401(k). Allocate is an equal opportunity employer. We value diversity of thought, background, and experience.
WHY THIS IS WORTH YOUR TIME
Most AI product roles are a feature bolted onto someone else’s product. This one is the product. You will own the layer that decides whether clients trust and understand their portfolios, from the extraction intelligence that makes the data reliable to the analysis that makes it meaningful, and you will be measured on whether it is probably right. If you want to build AI that real people stake real decisions on, this is the seat.