Ran Product Operations across 6 teams and ~50 workstreams for Studocu's next product bets.
Context
Studocu was pushing into AI study tools, class recordings, and agentic study experiences at scale. I joined as Senior Product & Operations Manager to bring operational rigor to a portfolio of bets spanning product, mobile, growth, marketing, AI, engineering, and platform.
What I built
I authored a scored distribution model assessing 16 acquisition channels, pairing each with a specific content artifact and entry mechanic, and explicitly cutting 10 channels with documented reasoning grounded in market evidence. I converted the analysis into a 12-initiative, 4-week test plan with named owners, kill criteria, and a ~€5k test budget. I built and owned weekly traffic and cohort reporting across 4 dashboards, designed an off-platform distribution experiment with ~100 course artifacts and per-channel attribution, and led external publishing for a new audio product from zero — show taxonomy, hosting partner, production pipeline, legal review, creative assets, and ops model. I also built a portfolio of 11 growth and distribution opportunities for class recordings, including a warm-start beta targeting a 9,400-user dormant cohort.
Outcome
Replaced ad-hoc reporting with a standing cadence, closed measurement gaps blocking the team from reading its own KPI, and gave leadership an evidence-based view of which channels to bet on — including the finding that AI-assistant referrals accounted for only 0.15–0.5% of site traffic, which killed the internal 'get cited in AI answers' thesis as a traffic replacement.
I can operate inside a high-scale consumer platform, structure ambiguity across dozens of workstreams, and turn analysis into shipped initiatives with clear owners and kill criteria.
I can operate inside a high-scale consumer platform, structure ambiguity across dozens of workstreams, and turn analysis into shipped initiatives with clear owners and kill criteria.