Senior/Staff Applied Scientist (시니어/스태프 응용 과학자)

Moloco builds some of the most powerful AI advertising solutions in the world. Our name—short for "machine learning company"—reflects our core mission: democratizing access to the advanced AI that has historically been reserved for tech giants. Led by machine learning pioneers who built some of the most successful ad systems at Google, including YouTube's monetization engine and key search advertising technologies, we're transforming how businesses grow and compete in the digital economy.

Responsibilities

Drive ambiguous signals to defensible root cause (KPI regression, a lift number that looks too good, unexplained cost increase, model degradation after upstream data change); Ship the fix (new feature, reformulated objective, recalibrated model, changed bidding policy, or correction upstream in data); Design evaluation that survives scrutiny (online experiments and offline evaluation in non-stationary auctions, interference, delayed conversions); Leave behind methodology and tooling for other teams; Communicate to ML engineers, infrastructure, product, and account teams

Requirements

Ph.D. in computer science, statistics, operations research, economics, or a related quantitative field, plus 2+ years of industry experience — or 5+ years of industry experience solving large-scale ML, optimization, or systems problems without one; Strong applied statistics and causal reasoning: experiment design, working with confounded observational data, and the judgment to recognize when a result is not real; Fluency in Python and SQL against large datasets (Spark, BigQuery, or equivalent); Experience owning a model or algorithm in production — including what happened to it after launch; Clear written English (a large share of this role's impact takes the form of a document that changes what a team decides to do)

Nice to have

Ads, recommendation, search ranking, marketplaces, or another domain with real-time auctions or bidding; Probability calibration, delayed feedback, or selection bias in logged-bandit data; CTV, attribution modeling, or incrementality measurement; PyTorch or TensorFlow applied to tabular or sequential production data at scale