An Analyst Role for People Who Actually Read the Footnotes
AI and machine learning move fast enough that most companies are reacting to yesterday's headlines instead of tomorrow's shifts. A new model architecture gets announced, a competitor quietly pivots their pricing, an adjacent market opens up overnight — and somebody has to be watching closely enough to catch it before it becomes obvious to everyone else. That's the job. This is a fully remote AI/ML Market Research Analyst position, paying $145,500 a year, supporting a client engagement through Naukri Mitra. You'll work most days independently, setting your own research schedule, but your findings feed directly into decisions other teams are making. That means the collaboration side matters just as much as the solo research itself, even if most of your calendar looks quiet from the outside.
The Work Itself
Some weeks you'll be buried in a competitor's product documentation trying to figure out what they actually built versus what their marketing claims. Other weeks you're running interviews with industry contacts, or stitching together a forecast model from a messy pile of secondary data that doesn't quite agree with itself. There's no single template for how the work unfolds — a lot depends on what question is live that month. The through-line, though, is turning scattered information into something a decision-maker can act on without needing a follow-up meeting to explain it.
- Track AI/ML technology trends, funding activity, and competitive moves across relevant industries.
- Pull data from surveys, interviews, industry reports, and financial filings, then reconcile the parts that disagree with each other.
- Keep a running competitive intelligence file that stays useful, not just accurate.
- Write reports and whitepapers that a non-technical VP and a technical product lead can both get value from.
- Build charts and slide decks that carry the argument without needing you in the room to explain them.
- Sit in on planning conversations with product, marketing, and sales to figure out what they actually need answered.
- Build simple forecasting models to estimate market growth and flag where disruption is likely to hit first.
- Push back on research methods that have gone stale and suggest better ones when you find them.
What You'll Need to Bring
There's flexibility on where your background comes from, but a few things aren't negotiable. If you're missing one of these, this probably isn't the right role yet — and that's fine; plenty of strong candidates grow into it over a year or two elsewhere first.
- Bachelor's degree, minimum.
- At least 5 years doing market research, data analysis, or strategic analysis work, with real exposure to AI/ML specifically.
- Comfort with a data analysis tool — Python, R, or SPSS all count.
- Track record designing and running both qualitative and quantitative research, including surveys you built yourself, not just ones you administered.
- Writing that holds up under scrutiny from both engineers and executives.
- The ability to run three or four research threads at once without one quietly falling apart.
Good to Have, Not Required
- Master's degree in a related field.
- Working knowledge of Tableau or Power BI.
- Background specifically in B2B tech market research.
- Some exposure to predictive modeling for market sizing or growth forecasts.
- A couple of past reports or decks you're proud enough of to walk us through.
Compensation and What Comes With It
The salary is $145,500 a year. Past that number, there's a handful of things that come standard rather than as negotiated extras.
- Fully remote, with flexible hours as long as you're available during core overlap windows.
- Health coverage for you and your dependents.
- Real paid time off — the kind people actually take without apologizing for it.
- A yearly budget for courses, certifications, or conferences tied to AI/ML or research methodology.
- Annual bonus tied to the impact and quality of your work, not just hours logged.
Who Tends to Do Well Here
People who succeed in this role usually share one habit more than any other: they don't trust the first clean answer they find. If a stat looks too tidy, they go check the methodology behind it before it makes it into a report. They can also write a two-page summary that a busy executive will actually finish reading start to finish, which turns out to be a rarer skill than it sounds — most research gets ignored not because it's wrong, but because nobody made it easy to absorb.
You don't need to code machine learning models or build them from scratch. You do need enough technical grounding to ask a sharp question in a room full of engineers, and to notice when a vendor's claims don't quite hold up against what the data actually shows. Curiosity carries a lot of weight here. So does the patience to sit with an unfinished answer for a few more days instead of forcing a conclusion that the evidence doesn't yet support.
How the Team Works
You'll report to a small leadership group and coordinate regularly with product, marketing, and sales. Nobody's watching your calendar hour by hour, and there's no expectation that you're online at fixed times outside of shared meetings. But your research gets used in real conversations, often within days of when it lands, so disappearing for two weeks and resurfacing with one giant report isn't the rhythm that works well here. Shorter, more frequent check-ins tend to serve everyone better. Feedback comes fast, revisions happen without drama, and the goal is always the same: research that actually shows up in decisions, not just in a slide deck nobody opens again.
Applying
Send your resume along with a short note describing one research project you're genuinely proud of, and what surprised you while working on it — the surprise is often the more interesting part. We're less interested in a polished summary of your career than in seeing how you actually think through a problem. Applications are reviewed as they come in, not in a single batch at the end, so there's no advantage to waiting. If your background looks like a strong match, expect to hear back directly, usually within a couple of weeks.
