£33,000 IC1 Insights Mae Anderson
Location: London (office-based, ~4 days per week)
native has been building for ten years and still runs like a startup: small, fast, and unsentimental about how things get done. We run a managed marketplace that connects students, Students' Unions, universities and advertisers. We increase student engagement, we help Students' Unions fund themselves properly, and we give advertisers a measurable route to a student audience. The closer those three line up, the better the business works.
Insights is where that audience becomes legible. We turn survey data, behavioural signals and platform data into the research brand partners buy and the segmentation that shapes how they reach students. We're looking for graduates who want real work immediately, learn at speed, and grow into something bigger.
We value clarity of thought, good judgement when the pressure's on, and the instinct to build structure where there isn't any.
You might be right for this if:
You think from first principles and build answers from the ground up
You can decide when there's no map, and you build structure where there isn't any
You care that the work is right, so you check it
You have range. You've done real things that demanded resilience, judgement or initiative
We're open to a wide range of degrees. Intellectual sharpness and structured thinking turn up most often in economics, statistics, the sciences, social sciences, geography, maths or computer science, though strong thinkers come from plenty of other backgrounds too. If your path is less typical, tell us how it shaped the way you think and why that stands up.
This is a broad data analyst role. The work runs from the survey and platform data we collect to the reports and analysis that go in front of partners. The mix of data analysis, survey research and visualisation shifts week to week, and we expect you to move across all of it.
You'll be hands-on with:
The analysis behind our insights, from raw data to the charts and the written finding
Survey data from Campus Voice and our commissioned studies: cleaning it, weighting it, and reading what it actually says
SQL against our BigQuery platform, pulling and shaping the datasets the team runs on
Clear visualisations for our reports, and the charts and numbers that feed our commercial pitches, from local advertisers to national brands
Crosstabbing survey and behavioural data against our student personas and segments, so a commercial pitch can show an advertiser exactly who it's reaching and how the segments differ
Keeping survey instruments, notebooks and documentation in a state where the research runs again next quarter without an archaeology dig
Working with the engineering team to sharpen the datasets and pipelines the insights work leans on, and flagging what's slow or fragile because you're the one using it
We build with agentic coding tools, and you will too. It's how an analyst here turns a question into a checked answer in an afternoon, work that used to take a week.
Used well, these tools ask more of you. The model is fast and often wrong in ways that look right: a query that runs clean and returns the wrong number, a chart that's plausible and misleading. So the job is judgement. You frame the question and decide what a good answer looks like before you let the model near it. You treat what it gives you as a first draft and check it, and you catch the analysis that's confident and quietly wrong. You own the output, including the parts the model wrote, and you can defend it with the tool closed.
If that sounds like more work than just doing a small analysis by hand, sometimes it is. That's the trade for everything larger that now fits in a day. The analysts who get the most out of these tools are the ones who were already rigorous. That rigour is what we're hiring for.
You've excelled at something, and we're not precious about the form: first-class honours, a Dean's List, a research result, a project you couldn't leave alone. We're reading for rigour and clarity of thought
You can reason statistically: you understand sample bias and weighting, what a significance test is actually telling you, and how to interpret a regression. From coursework, a competition, or a real project
You're commercially curious: genuinely interested in how brands reach audiences and what makes a finding worth paying for
You've worked with real, messy data: cleaning datasets, validating a result, designing a schema that holds. This can be from coursework, a competition, a personal project, wherever
You write proper Python, in pandas and numpy, in addition to being able to structure it into functions and shared utilities that the next person can run
You write SQL that stays correct when real data is messier than the textbook example: the duplicates, the NULLs, and the joins that quietly break a query that looked fine
You can take a result and make it clear, in a sentence and in a chart, for someone who wasn't in the data with you
You teach yourself the tool you need before anyone tells you to: BigQuery, dbt, a plotting library, git, survey tooling
Bonus points if you've taken a piece of analysis end to end that other people used. A study, a dashboard, a report, a model. Anything real
This is a six-month engagement, and we mean it as a proving ground for a permanent hire. Do well and you move into a promoted, permanent role at the end of it.
The trajectory is the offer here. You're in live commercial work from week one, with real ownership of analysis that partners read and pay for, and the breadth is the point: in six months you'll have run analysis end to end, cut survey data against our personas for commercial pitches, and seen your work reach brands. That range this early is rare, and almost impossible to get on a scheme that keeps you in one lane while it decides what to do with you.
During the process you'll talk to grads who joined this way, so you hear how it actually went, straight from them.
You'll work from our London office at least four days a week, with one optional day remote. We move fast and decide fast, and most of that happens face to face.
We don't want a cover letter. Answer a few questions instead, so we can see how you think
A trade-off you had to make, and how you decided
A problem you tackled without much guidance
A piece of analysis or a number you'd present differently to make it clearer, and how
A time you chose what not to do, and why
Include a recent CV, or a link to your LinkedIn or equivalent.
And if your route here isn't the obvious one, a degree we didn't name or skills you taught yourself, apply anyway. We're reading for how you think and whether the core is there. Don't rule yourself out.
We hire on a rolling basis. If this is the kind of challenge you're ready for, get in touch.
We're building an equitable environment where everyone at native can do the best work of their lives. Diversity and inclusion sit at the centre of that, and we put real support behind helping all of our people grow here.
