Machine Learning Scientist 5- Forecasting Aggregation

Advanced degree (PhD or Master's) in Statistics, Mathematics, Computer Science, or a related quantitative field. 5+ years of relevant experience building machine learning models on large-scale data. Deep expertise in supervised learning (e.g. gradient-boosted trees, regression, and related methods) with a strong bias toward interpretable, explainable models. Strong feature engineering skills and familiarity with feature stores and standard ML lifecycle practice (versioning, evaluation, monitoring, retraining). Proven ability to prototype algorithms and validate them rigorously against production data. Strong programming skills in Python and strong SQL. Working knowledge of ad-serving and campaign concepts — how campaigns are delivered and what creates delivery risk: targeting, frequency caps, contention, bidding, pacing, budget planning, and the core campaign objects/attributes; and the metrics that matter (reach, frequency, impressions, clicks, outcomes). You should understand both the supply side (ad-serving rules and inventory behavior) and the demand side (campaign attributes and advertiser goals). Ads experience is strongly preferred. Ability to work independently, drive your own projects, and make compelling cases for prioritization. Ability to communicate technical and statistical concepts clearly to audiences at many levels. Experience at a DSP, SSP, or publisher-side ad platform where predicting campaign outcomes at scale is a core science problem. Familiarity with our ML stack (Metaflow) or comparable large-scale ML tooling. Experience partnering with ML engineers to ship and monitor production ML systems. Experience creating data products, dashboards, or explainability tooling for non-technical stakeholders. Experience applying GenAI to boost developer/research productivity.