Product Manager - AI

Phyllo is a data gateway that allows social data to be accessed from source platforms (e.g. YouTube, Instagram, TikTok, Twitch, Upwork, Shopify, and more). We build the underlying infrastructure that connects with every creator platform, maintain a live data feed to the systems used by these platforms to manage creators’ data, and provide a normalized data set so that businesses can use creators’ data in a simple yet impactful way.

Clayface is our AI analyst for consumer brands, built on top of that data foundation. Insights and brand teams at consumer goods companies spend days pulling numbers together from retail, social, and syndicated sources before they can start analysing anything. Clayface does that work for them. You ask it a business question and it comes back with an answer: what changed, what is likely driving it, and what to look at next, with the source behind every number.

More info at:

• Clayface: https://www.clayface.ai/ 
• Phyllo: https://www.getphyllo.com/
• Our Crunchbase profile: https://www.crunchbase.com/organization/phyllo

We are looking for a Product Manager to lead Clayface. You will own what the analyst can do, how it arrives at an answer, and how much a brand team can trust what comes back. That means understanding the questions insights and category teams actually ask, designing the steps the product takes to answer them, and making sure those answers hold up when someone senior pushes back in a meeting.

This is a hands-on role. You will be close to the product every day: trying things yourself, reading what it produced, and deciding what to change. It is not an AI strategy role, and it is not a role where you write a brief and wait for engineering to come back with something.

• The roadmap for Clayface: which business questions we answer well next, which types of analysis we take on, and what we deliberately leave out.
• Spending real time with insights, brand, and category teams at consumer goods companies. You need to understand how decisions actually get made, what gets asked in the room, and where teams currently get stuck.
• Turning that understanding into a product that gives useful answers rather than impressive-looking output. An analysis that is technically correct but does not help someone decide anything is a failure.
• Writing clear requirements an engineer can build from without needing a second meeting.

• Clayface is not a single prompt. It is a sequence of steps: find the right data, match it across sources, run the analysis, check the result, and explain it clearly. You will design how those steps fit together, which ones the system decides for itself, and where a person should stay in the loop.
• Making the product work across the messy range of questions real customers ask, not just the handful that demo well. That includes deciding what should happen when the data is thin, when two sources disagree, or when the honest answer is that we cannot tell yet.
• Working with engineering on what the platform needs so we can add new data sources, categories, and customers without rebuilding it each time.
• Balancing quality against speed and cost. How long an answer takes and what it costs us to produce are product decisions, not just engineering ones.

• Deciding what a good answer looks like for each kind of question, and making sure we measure it rather than assume it. You will set the bar and work with engineering on how it gets checked. The specific methods will change as the product grows, and we expect you to have opinions on that.
• Making sure every number can be traced back to a source and a time period. Customers make expensive decisions on this output, and a confident wrong answer costs us far more than no answer.
• Testing properly before things go live. AI products fail quietly rather than loudly, so a few good examples is never enough evidence to ship.
• Owning the questions enterprise buyers ask about data handling, security, and how their data is used.

• Partnering closely with engineering, design, data, sales, and customer success, and making sure the people who sell and support Clayface understand what it does well and where it is still weak.
• Connecting the product to outcomes that matter to the business: customers who keep using it, accounts that grow, and deals that close faster.
• Being the voice of the product internally and with customers, and bringing what you hear back into the roadmap.

• 5 years in product management, including at least one AI product or feature that real users used and that you measured after launch.
• You are hands-on with AI products. You have written and improved prompts yourself, looked closely at what the system produced, and worked out why it went wrong.
• A working understanding of how AI products are put together today: how a model gets the context it needs, how it uses tools and data sources, how multi-step workflows are strung together, and where these systems usually break.
• Real experience with evaluating AI output. You have set up ways to test quality systematically and used the results to decide what to change.
• You care about evidence. You check whether an answer is genuinely supported by the data, and you say so when it is not.
• Comfortable with data. You can write SQL or work in a BI tool, and you are appropriately suspicious of numbers that look too clean.
• Enough technical depth to have a real conversation with engineers about trade-offs, cost, speed, and data dependencies without needing everything explained from first principles. You do not need to write production code.
• Clear writing. A good spec from you should prompt specific questions about edge cases rather than confusion about what you meant.
• Comfortable saying no, including to people who are excited about something that demos well.

• A background as an engineer, analyst, or data scientist
• Experience with consumer goods, retail, or market research data: syndicated data, retailer sell-through, digital shelf, or social listening.
• Experience building products that sit on top of messy data from many different sources.
• Familiarity with tools for testing and monitoring AI products.
• Experience with B2B or enterprise customers, including what their security and procurement teams ask for.
• An advanced degree in a technical or business field.

We invest in our people and believe in hiring high-potential, humble individuals who can rapidly grow their responsibilities as the company scales. You will infuse insights and ideas into business decision-making, solutions strategy, and the innovation roadmap for each product.

Most AI product roles are about adding a feature to something that already exists. This one is different. Clayface is trying to do the job of an analyst, on real data, for customers who will tell you straight away when the answer is wrong. If that is the kind of problem you want to work on, we would like to hear from you.

If you have worked on an AI product before, tell us about something it kept getting wrong and what you did about it. That will tell us more than a cover letter.