A dashboard that nobody trusts is worse than no dashboard at all. Getting stakeholders to actually rely on a number takes clean data, a clear method, and someone willing to explain the reasoning behind a trend, not just the trend itself. That is the job here.
We need an Applied Research Scientist who can take messy datasets and turn them into decisions people act on. The role is full-time and fully remote, open to candidates anywhere; there is no office to report to and no city requirement attached to it. Experimentation and applied research work has a specific rhythm to it: a hypothesis, a pull of the relevant data, a check on whether the result actually holds up under scrutiny, and then the harder part of explaining it to people who were hoping for a different answer.
The category name, Experimentation and Applied Research, sounds academic, but the actual work is closer to fast-turnaround business analysis than anything resembling a formal lab study. Findings need to be useful within days, not published within a year.
Some weeks lean heavily into building; others are mostly presenting and defending a set of findings to people who were not expecting the answer they got. Both matter equally, and neither is optional. A stakeholder asking why signups dipped for two weeks in a specific region is a normal Tuesday, and the honest answer sometimes involves admitting the dashboard needs a fix before anyone trusts the number behind it.
This is not an entry-level research post, but it also does not demand a decade of tenure. Twelve months of hands-on applied research experience is the bar, paired with a bachelor's degree in statistics, business, computer science, or a related quantitative field. What matters more than the exact title on your last resume is whether you can defend a conclusion when someone pushes back on it in a meeting. People who struggle in research-adjacent roles are usually fine with the analysis itself and uncomfortable with the second half of the job, the part where a finding gets questioned out loud in front of a room.
Comfort with that friction matters as much as the technical skill. A statistics-heavy academic background transfers reasonably well too, as long as the pace adjustment from research timelines to business timelines does not come as a shock.
A stakeholder in marketing might ask why signups from a paid channel dropped after a pricing change. Answering that well means pulling transaction and signup data, checking whether the drop is real or a tracking gap, segmenting by channel and geography, and building a short deck that walks through what actually happened rather than just restating the question back with a chart attached. That full cycle, from question to defensible answer, usually takes anywhere from two days to two weeks depending on how messy the underlying data turns out to be.
SQL, Excel, and data visualization are non-negotiable starting points. Strong statistical analysis ability and genuine attention to detail round out the must-haves:
Prior applied research experience specifically, rather than adjacent analytics work, is a real plus but not an absolute wall if everything else about your background lines up. Someone coming from a market research or academic research background, for instance, often adapts quickly once they get comfortable with the faster pace of business decision-making compared to a formal study. Tableau or Looker experience is a nice bonus if SQL and Excel are already strong, since a lot of finished output ends up in one of those tools regardless of what was used to build it.
Salary for this role is 116,000 dollars a year, full-time. The benefits package includes:
Additional perks can vary depending on the employer running the search, so ask about specifics once you are in conversation with a hiring team. Compared with adjacent analyst titles, this salary sits at a level that reflects the presenting and stakeholder-facing side of the job, not just the raw data work behind it. Career growth from here usually runs toward either a more senior analytics role with direct ownership of a metrics area, or a research-manager track overseeing a small team of analysts. Neither path requires switching companies, though some people do.
Remote here means genuinely remote: no fixed hours beyond keeping reasonable overlap with your immediate team, and collaboration runs through shared dashboards, written async updates, and a handful of scheduled meetings each week rather than constant video calls. Remoteroles works with a range of employers on roles like this one, and most run a lean, low-meeting culture because the analysis work itself demands focus time that a packed calendar would only get in the way of. Most teams here run on a shared BI tool alongside a ticketing system for incoming requests, and a short weekly planning meeting sets priorities for the days ahead.
Expect one or two recurring stakeholder meetings a week where you present rather than just report, plus flexible working hours the rest of the time.
Interviews for this role typically include a short take-home or live exercise using a sample dataset, since seeing how someone approaches a messy, realistic problem tells us more than a resume can. That exercise is deliberately open-ended, since deciding what matters first, when nobody hands you a clean brief, is exactly what the job asks for every week.
Ready to apply? Send a resume plus one example of analysis work you are proud of; even an informal writeup works. Move on this one soon, since roles at this experience level tend to fill quickly once a strong pool comes in.