General Manager DSP

BidMachine is looking for General Manager DSP.

This role owns the commercial result of our demand-side platform (DSP) for in-app advertising on iOS and Android, from model to campaign to gross profit. The person in the seat decides which parts of the DSP get built, in what order, and which ideas get dropped. They make campaigns profitable for our first, affiliated games client, then bring external advertisers on board once the models are ready. Success is measured as gross profit per person in the squad, so the squad stays small. Our edge is supply and SDK data from our own exchange that other DSPs don't have. Turning that edge into installs that pay back is the job.

The platform is being built so that campaigns learn and scale without an account manager or an engineer stepping in. As more of the bidding and campaign work is automated, this role concentrates on what stays human: what to build and what to drop, the commercial calls with advertisers, and when an exception is worth asking for. One honest note: the DSP is early, it loses money today, the competitors you'll be measured against have been doing this for years, and each quarter of investment has to be argued for with results.

Location: Barcelona, Spain. 

Key Responsibilities: 

Own the commercial result:

• Own gross profit per squad employee and the P&L behind it, including experiment and hosting costs.
• Write the plan the business backs, and report against it every week.

Decide the build sequence:

• Decide which DSP components get built, in what order, and which ideas get dropped.
• Define what "good enough" means at each step: a baseline model on exchange SDK data, then install and ROAS models, then external advertisers.

 Make campaigns profitable:

• Win more install supply at a price that works, and lift return on ad spend for the first client.
• Fix the system rather than rescuing campaigns by hand.

Bring on external advertisers:

• Qualify in-app advertisers before they come on board, once the models are ready.
• Talk to the advertisers you serve every week.

Lead a small squad: 

• Lead data scientists, analysts and engineers, and decide who works in the squad.
• Bring performance UA know-how into the squad, by hiring, coaching or partnering.
• Stay hands-on in at least one area of the squad's work.

Know the market:

• Know how the machine-learning DSPs bid, what they charge and where they win, including from running campaigns on their platforms.

Experiment and write it down:

• Run experiments with a clear hypothesis, a healthy baseline and a reproducible result, and integrate the learning.
• Keep a decision log, so the squad knows who decided what and why.

Work across teams:

• Work with the exchange teams on supply and data, with engineering leadership on infrastructure and hosting cost, and with finance on how gross profit is counted.

Requirements:

• Has built or scaled a performance DSP, ideally including a cold start. Experience running UA at scale on third-party DSPs, without having built one, is also considered.
• Commercial ownership. You have owned a P&L or a commercial number, and you see clients and competitors as clearly as you see the system.
• Performance UA economics for in-app advertising: CPI, ROAS and the long tail of ad revenue.
• Has led data scientists and engineers whose work you could judge.
• Demonstrated AI experience, required. Practical, hands-on use in your own commercial or product work, with the ability to describe the specific workflows you improved, the results, and how you validated the output.
• Strong data analysis capability. You can pull and interrogate campaign, bidding and margin data yourself (bid rate, win rate, cost per install, ROAS by cohort). You can explain to an advertiser or a non-specialist what moved and why.
• Based in or relocating to Barcelona, and able to work office-first.