Remote Growth Marketing Manager, full-time, paying up to $92,000 a year, open to candidates anywhere. The job is built around running structured tests rather than executing a fixed marketing plan, so the person in this seat spends more time forming hypotheses than following a calendar. Curiosity about why a number moved matters more here than a strong opinion about what should work.
Who this fits
A bachelor's degree covers the education side, generally in business, marketing, or a similar field, paired with two and a half years spent actually running structured growth tests rather than just executing someone else's roadmap. Comfort reading data closely enough to trust or challenge a result is assumed coming in, not something picked up on the job. Basic statistical literacy, enough to know when a sample size is too small to trust, matters more than people expect walking into this.
Two and a half years is generally enough to have run at least one experiment that looked promising early on but fell apart as the sample size grew, and to have learned not to call a win too early because of it. That patience matters more here than almost any other single trait. It's also usually enough time to have argued for a test that leadership was skeptical of and to have been wrong at least once about how it would land.
The work itself
- Come up with a test worth running, whether that's a new onboarding flow or a tweak to how new users get activated, and see it through to a real result
- Dig into where people are dropping off in the funnel and figure out which step is actually worth fixing first
- Work directly with whoever's building the product and the engineers shipping it, since most growth ideas need actual code to go live
A referral program that looked like a clear win in the first two weeks can quietly turn out to be mostly gamed by a small group of users creating fake accounts to collect the reward, and catching that before it skews the whole month's numbers is a real part of the job. Some weeks are almost entirely analysis. Others are spent in planning meetings arguing for why a particular test deserves engineering time over three other requests, each backed by someone equally convinced their idea should go first.
An onboarding change that boosted activation by double digits once quietly hurt three-month retention, since the faster path skipped a step that had taught people how to actually use the product. Wins that only look good in the short term are one of the harder traps to spot here, mainly because the damage doesn't show up until well after the test has already been called a success and moved on from.
Skills that come up
Growth experimentation sits at the center, backed by real funnel analysis skill and A/B testing that goes beyond just picking a winner at the end. Comfort inside at least one analytics tool matters just as much, since none of this works if you can't actually see what happened after a test goes live. Cross-functional collaboration ties it together; almost nothing here gets built without buy-in from product and engineering, so knowing how to make a case for a test's priority matters as much as designing the test itself. A well-designed test that never gets built because nobody prioritized it delivers exactly zero value. Beyond that core set, a few things aren't required but help a lot: comfort writing basic SQL to pull your own numbers instead of waiting on a data team, experience inside a tool like Amplitude or Mixpanel specifically, familiarity with a formal growth framework like AARRR or a defined north star metric, and any direct experience partnering with a data scientist on something more statistically rigorous than a simple split test. Someone who's already had a test rejected for weak methodology once tends to design cleaner experiments the second time around.
Pay and what comes with it
Base pay runs up to $92,000 a year, with a bonus tied to how experiments actually performed layered on top of that. The standard package covers medical costs, a real stretch of paid days off, and matching contributions into a 401(k). Bigger companies hiring for this kind of role sometimes go further, picking up part of a course fee or offering an annual stipend for outside learning, though that's more common at scale than at smaller companies. Worth asking directly whether the bonus is based on individual experiments or the team's overall growth number for the period, since those two setups reward fairly different behavior.
Remote setup
This is a remote growth marketing manager role with no location requirement attached, so most collaboration happens through shared dashboards, written test proposals, and scheduled syncs rather than dropping by someone's desk. Naukri Mitra sees candidates for roles like this land here from product or data analyst backgrounds fairly often, mainly because both jobs already build comfort forming a hypothesis and checking it against real numbers.
Working this way alone means a proposed test has to be argued for in writing well enough that an engineer who's never met you agrees it's worth their sprint time. Vague requests get deprioritized fast when nobody's in the room to clarify them in person. A test proposal with a clear hypothesis and expected impact tends to get built; one that just says "let's try this" usually sits in a backlog indefinitely.
Applying
Send a resume along with one example of a growth experiment you ran, including what you tested, what you expected, and what actually happened; wins and misses both count. Shortlisted candidates will walk through a sample funnel during the interview and talk through where they'd look first for a leak. There's usually a short follow-up conversation after that with whoever currently owns growth strategy, mostly to see how your prioritization instincts compare to theirs. Expect the process to run about two to three weeks from application to offer.
