About the role
In most media buying teams (teams that buy paid traffic at scale and monetize the conversions), analytics stops at the ROI column inside the tracker. We work differently. ROIcamp has been in the dating vertical since 2011, and we count money across the entire funnel: click, registration, subscription, and rebill, the recurring charge a subscriber pays after the first payment and the point where most of the revenue is actually made.
The buyer bonus formula is open to the whole team, and it runs on data that you collect and validate. Your numbers become someone’s paycheck, so when a buyer opens the dashboard at month end, every cell has to be trustworthy. Building and holding that level of trust is the core of this job. You report to the Head of Media Buying.
What you’ll do
- Stitch the tracker and product analytics into a single pipeline, so that a click recorded in the tracker (the system that logs ad clicks, conversions, and spend for every campaign) can be matched to the payment it eventually produced.
- Build BI dashboards that cross spend, revenue, and LTV by source, campaign, GEO (the country or region a campaign targets), and individual buyer.
- Run cohort analysis covering conversion to subscription, retention, rebill dynamics, and LTV forecasts 3–6 months out.
- Validate bonus calculations and reconcile the underlying data before payout, so that no number reaches a buyer unchecked.
- Host weekly reviews with the buying teams on which cohorts pay back, which campaigns deserve more budget, and where LTV is sagging.
- Set up anomaly alerts for fraud patterns, discrepancies between tracker and product, and sudden drops in conversion.
- Agree with the tech lead on which events and parameters tracking has to pass through so that the two data sources reconcile.
Requirements
- 2+ years in performance or product analytics.
- Confident SQL together with at least one BI tool such as Tableau, Looker, or Metabase.
- Hands-on cohort analysis and LTV forecasting from real work rather than coursework.
- A working command of subscription funnel unit economics, including why the rebill matters more than the first payment.
Nice to have
- Python for building pipelines and models.
- Familiarity with trackers such as Keitaro, Binom, or Voluum, and with S2S postbacks (server-to-server callbacks that report a conversion back to the tracker).
- A background in a dating or subscription product.
What we offer
- Fully remote work, judged on output rather than online status.
- Product data accumulated since 2011 to work with, which means real cohorts rather than a startup with no history.
- Direct visibility, since buyers and the Head open your dashboard every morning.
- A tooling budget approved as soon as you can justify it with numbers.
- Salary paid on a fixed date.
Growth path
Analyst, then Senior Analyst, then Head of Analytics with your own team as the department scales. The promotion criterion is straightforward: the system you built runs without you steering it by hand.
Compensation
$1,800–2,800 base plus result-based bonuses. Payouts on a fixed date.
Hiring process
A 30-minute screening call, then an interview, then a compact test of up to 3 hours built on a real case with anonymized data, then an offer.
Questions about this role
What is the current stack and what will I build?
A tracker and product analytics exist as separate systems. Your first task is connecting them into one picture: data pipeline, cohort reports, team dashboards.
Where does the data come from?
Two sources: the tracker (clicks, conversions, spend per campaign) and product analytics (payments, retention, LTV). The key job is making them reconcile.
Do I influence the bonus formula?
Directly: buyer bonuses are computed from data whose quality you own. It is a high-trust zone — which is exactly what makes it interesting.
How much ad-hoc vs systematic work?
Target ratio is 30/70. Early months will be more ad-hoc while dashboards are built; then self-service should cover most questions.
Do I need to know traffic arbitrage beforehand?
No. You need funnels and unit economics — we will explain arbitrage specifics (sources, campaigns, consumables). Half the jargon is learned in month one.
Worth reading before you apply
- What a Media Buyer Does: Duties, Skills and Career Path — What a media buyer does: duties, skills and tools, how the role differs from a targetologist, what changes by vertical and how the career ladder works.
- Dating Traffic in 2026 — Sources, Costs and What Converts — Dating traffic in 2026: sources, CPL/PPS payouts across GEO tiers, funnel math to first payment, Meta and Google compliance, and quality metrics.