Product Manager, Distribution & Adoption Swym

Remote-first · Full-time · Bengaluru-based team. Sits within the Founder's Office / Product org, working closely with Product, Growth, and CX.

Every day, millions of shoppers save products they want but are not ready to buy. Most brands never know it happened. Swym fixes that. We capture what shoppers want at the earliest and most specific point in their journey, and we make that signal usable across every channel a brand has, until the purchase completes. 48,000 brands use Swym today.

Building the feature is rarely the hard part anymore. With AI doing first-draft engineering, most features move from idea to working prototype fast. What doesn't move on its own is adoption: whether a merchant who has access to a capability actually turns it on, uses it, and keeps using it. That gap, between shipped and adopted, is what this role exists to close.

This is not a generalist PM role and not a PMM role. It is a hybrid, weighted deliberately: roughly 60-70% of the job is distribution and adoption, 20-30% is build. Build is a given, with AI as leverage. The hard, differentiating work is figuring out who needs to know about a capability, in what format, through what channel, and whether it actually changed behavior once they saw it.

We are a lean team that runs on AI and GitHub. No wiki. If it matters, it is in the repo or it does not exist.

When a merchant workflow needs instrumenting, AI drafts the event schema. When a channel needs a first-pass message, AI writes it. When we need to know which merchants are underusing a feature, AI pulls the query. Humans decide what the data actually means, which channel earns the spend or the send, and what gets shipped to a merchant's inbox or in-app surface. That model runs across this role specifically: positioning is not this role's job, activation is.

In your first 90 days — get fluent in our data and our merchants

Learn to query and read Swym's own activation and usage data without waiting for someone to hand you a dashboard. Identify at least one feature that is live but underused, and be able to say why, backed by data you pulled and checked yourself, not a summary someone gave you.

Build your own AI-assisted workflow for pulling usage data, drafting channel content, and checking your own read of a dataset before you act on it. Ship one small distribution push, end to end, even if it is narrow in scope.

At six months — a channel or format decision you made, and can defend with data

You have run at least one adoption push where you chose the channel and format based on what the data actually showed, not on what was easiest to produce. You can point to a specific moment where you almost drew the wrong conclusion from a metric that looked like a win but wasn't, and caught it before it shipped.

At least one merchant-facing asset you wrote (a nudge, an email, an in-app message) is live and something Sales or CX has used without being asked to.

At twelve months — the adoption motion is yours, and it compounds

Feature adoption curves for the features you've touched look different because you were here. You have a defensible point of view on which channels actually move behavior for Swym's merchants versus which ones just generate activity. You are building the org's muscle for distinguishing the two.

The question at twelve months is not how much content shipped. It is whether adoption would look the same if you hadn't been here.

You are fluent with data, not adjacent to it. You can pull, join, and interrogate a real dataset yourself, and you know the difference between a metric that looks like success and one that proves it. If someone hands you a summary, your instinct is to check it against the source before you act on it.

You have real intuition for distribution and PLG: you know that awareness is not adoption, that the channel with the most clicks is not automatically the channel that works, and that a feature nobody discovers is functionally a feature that doesn't exist.

You are AI-first in both thinking and execution. You use AI as a default for querying, drafting, and synthesizing, and you know exactly where your own judgment has to override it. This is assessed during the process, not assumed on your word.

Your outreach is good: specific, rooted in real product knowledge, and confident without overclaiming. A merchant reading something you wrote should feel like you know their business, not like you ran a template.

Three to six years of experience, as a guideline rather than a cutoff. Product, growth, or PMM backgrounds are all plausible entry points, provided the person has done real hands-on distribution work, not managed it from a distance.

Most roles like this get split across two people who don't talk to each other, one who builds and one who distributes, and adoption falls in the gap between them. Here, one person owns both ends of that gap, with a data layer real enough to actually prove what worked.

If your idea of a good year is: I found the feature nobody was using, figured out why, and fixed it with a channel decision I can defend with data, you will be happy here.

We are strong where it counts: precise, anonymous intent capture, and activation depth across the merchant marketing stack. We are open about where we're still early: Back in Stock and Gift Registry are in maintenance mode pending a relaunch, Swym for Sales (Retail and B2B) is past proof-of-concept but not yet at scale, and our MCP integration does not yet output shopper-level intent data. Part of this job is knowing exactly where that line sits and never overclaiming past it, to a merchant or in a campaign asset.

If that open problem sounds like the interesting part rather than a warning, that is a good sign.

Compensation: Competitive cash and meaningful equity. We will talk specifics early.

Please apply directly through this LinkedIn job post.

If you are shortlisted, here is what happens next. We will reach out within a week. You will receive a 90-minute build assignment: a real dataset, an open brief, no predetermined answer. Use whatever AI tools you normally use. We evaluate thinking and judgment, not just polish.

What happens to what you send us: if you do not advance, your CV and any work you share will not be used beyond this process. We will not retain it beyond 90 days.