? About Respondent

Respondent is building the infrastructure that powers participant recruiting for the research industry. Our mission is simple but ambitious: to make it effortless for researchers to find high-quality human participants - whether they're testing a product, exploring a market, or validating an idea.

From our founder's first recruits on a NYC subway to a global panel of 4 million participants across 150+ countries, we now support research for companies like Microsoft, Dropbox, and P&G;. Our panel is one of the most diverse and responsive in the industry.

We're a 35-person team across Sydney, Manila, and the US. We're intentional about collaborating in person - serendipitous hallway conversations, whiteboarding sessions, and quick alignment cycles help us move faster and build something great together.

We've gone from 0 to 1, recently reaching profitability and intending to stay so. We're scaling with intention and reinvesting in the business to grow our platform, panel, and team. Joining at this stage means your work will have a direct impact as we take Respondent from 1 to 10.

? The role

You'll report to our Head of Fulfilment and own the analysis behind how participants find us, sign up, get matched, stay active and come back. That's the whole participant lifecycle, plus fraud rules and marketplace quality running across it.

You proactively review and analyse our data and ask questions - why are 39% of active participants filtered out before matching, what is a participant in this segment actually worth, is this rule catching fraud or catching customers - and you frame it, work out what's going on, and bring engineering a sized problem with options. Engineering builds the fix it's handed.

Alongside that, a few ad-hoc requests land every week from across the business. Expect roughly a third of your week to go here, and expect to turn the repeat ones into a saved query or a dashboard line so they stop coming back.





Our ML engineer owns ranking, matching, churn prediction and the fraud models; you don't ship the changes either - engineering does. What you own is the question and the answer.

? Your impact

- Work out what a participant is worth: Build LTV for each supply and demand segment and turn it into what we're allowed to spend acquiring one. That number becomes the budget rule our Performance Marketer runs on, so it has to hold up.
- Make marketplace quality visible: Scoring for participants and researchers, the rules that reward the good ones and catch the bad ones, and the dashboards and alerts that watch it. None of this exists yet.
- Pull the activation and retention levers: Seat allocation for current and repeat participants, activation incentives, the power-user badge, reactivation for lapsed participants.
- Shape the front of the funnel: What we ask at sign-up and what that predicts, how much friction to trade for a richer profile, when to waitlist a segment that's already oversupplied, and where enrichment should happen instead.
- Hand engineering evidence: When something's broken, chase it until you can explain it, then brief it properly so it gets built once.
- Fix what fraud rules are costing us: Run the risk rules with a monthly read on each one and a call to keep, loosen or release. Build the route back for people we held by mistake. On one recent read, 99,671 of 252,721 recently active participants were filtered out before matching - some of that is fraud and some of it is us.
- Make the case for what's next:



The participant app is an open question with no analysis behind it. Size the demand and take the pitch to the exec team.

✨ What you bring

- 3 to 5 years solving problems with data: In any role and any industry. What matters is that you've taken an open question through to a decision someone acted on, not how many dashboards you've maintained.
- The right questions first: Of the data, and of the people who already know - engineers, support, account managers. The answer usually starts as a conversation.
- Experimental instinct: You know how to test something and how to measure it, and you set the KPIs and the tracking before a change ships rather than after.
- You understand how logging works: Enough to go and set up the tracking yourself, so the data is there when the question arrives.
- You chase the number: Until you can explain it. You say plainly when something doesn't add up, and you run your own work rather than waiting to be unblocked.
- You write for people who aren't analysts: A one-line takeaway, a table, and the derivation sitting next to anything surprising.

? Why you'll love it here

- Ownership: 21 projects are sitting in the queue waiting for someone to pick them up. You'll choose which ones matter and in what order.
- Commercial impact: You get to see and impact the full loop of a participant’s lifecycle while playing a major role in defining the numbers that set the acquisition spend.
- Set up to start fast: Locked definitions, a data map, query scripts and a read-only replica are waiting for you.
- First analyst in the team: The shape of the role, the tooling and what gets measured are all still open - you can build and set best practices.
- Stable but moving: A profitable business with startup pace and no runway anxiety.
- A small team across three continents: Sydney, Manila and the US, and small enough that your work is obvious to everyone.

📌 Growth Data Analyst - Fulfillment (Sydney)
🏢 Respondent
📍 Sydney