# AI/ML Product Manager at Disconetwork

- Company: Disconetwork
- What the company does: About Disco Disco powers a next-generation commerce media network that connects over 1,000 eCommerce advertisers to consumers on some of the world's most recognizable retail platforms, including Mindbody, Gopuff, Fabletics, and Knitwell Group (Chico's,... Backed by Felicis.
- Company website: https://www.disconetwork.com/
- Type: Startups (AI role)
- Level: Senior
- Location: New York, NY
- Work setup: On-site
- Pay: $190K to $220K base salary per year (USD)
- Posted: 2026-08-27
- Apply by: 2026-10-11
- Apply: https://ats.rippling.com/careers-at-disco/jobs/c9b377fc-506e-4859-8fe6-d52d5519e48f
- Page: https://www.1752.vc/careers/jobs/disconetwork-ai-ml-product-manager/

## About the role

Disco is a three-sided market. Publishers want more yield per load. Advertisers want more performance per dollar. Disco has to protect its margin in between. Every model decision trades one against the others: push CPMs up and CPCs drop, which hits margin. Optimize for publisher yield and advertiser performance suffers. Your job is to understand those trade-offs cold and turn them into better model decisioning, not better slides.

## What they're looking for

- Own the ML roadmap for our bidding and ad-serving engine: how we price, rank, and allocate every offer impression
- Drive precision across all three sides at once: publisher yield (revenue per load), advertiser performance (CPA/ROAS), and Disco margin. Own the trade-off call when they conflict
- Turn ambiguous optimization problems into hypotheses, evals, and shipped improvements. A CPA spike at Apple is your problem to chase to the source before it eats our ML engineers' time
- Own the decision logic that goes into the model: when we optimize for margin vs. publisher yield vs. advertiser performance, and how to make that call clearly
- Think in systems, not single metrics. A CPM change ripples into CPC, which ripples into margin. You reason through the whole cascade before acting
- Partner with the ML team on the full loop: hypothesis, model iteration, eval, A/B test, ship, monitor. You're a collaborative partner to the data science team, not a ticket writer throwing requests over the wall

Tags: Product