Senior Data Scientist

Build and improve censored bid-landscape models to estimate clearing-price distributions from partially observed auction data  Develop real-time win probability estimation models responsive to bid pricing dynamics Design and implement hierarchical lift estimation models with confidence-bound-based selection strategies Build conversion propensity models using sparse, delayed, and aggregate-only labels Develop look-alike audience modeling approaches using positive-unlabeled learning and embedding-based nearest-neighbor techniques Implement advertiser-level calibration strategies while independently monitoring ranking and calibration quality Design robust offline evaluation frameworks using inverse-propensity scoring, doubly-robust estimators, and importance reweighting Define exploration strategies and propensity logging approaches to ensure reliable downstream correction and evaluation Develop constrained optimization mechanisms for campaign objectives, pricing constraints, and volume targeting Contribute to data diagnostics, capability assessments, and evidence-based model recommendations Collaborate with the Customer team during post-launch tuning and performance validation cycles Prepare technical documentation and knowledge transfer materials for the Customer’s internal data science team Participate in architecture discussions and contribute to scalable ML platform design decisions 5+ years of experience in Machine Learning or Data Science with production-grade models measured against business KPIs Strong Python skills including numpy, pandas, and scikit-learn Strong SQL skills and experience working with large-scale datasets Deep practical experience with XGBoost, LightGBM, or CatBoost Strong understanding of regularization, calibration methods, and categorical feature handling Strong knowledge of probability, statistics, confidence intervals, and statistical power analysis Experience with feature engineering for structured and behavioral datasets Hands-on experience with Spark or PySpark Practical knowledge of experimentation frameworks and A/B testing methodologies Experience with advanced validation approaches including temporal splits, leakage detection, drift analysis, and slice-based metrics Understanding of explainability techniques such as SHAP and permutation importance Upper-Intermediate English level or higher WILL BE A PLUS Experience in AdTech modeling including CTR/CVR prediction, bid-landscape modeling, audience segmentation, and RTB mechanics Experience working with sparse, delayed, or censored labels Knowledge of attribution modeling, survival analysis, and positive-unlabeled learning Practical experience with counterfactual and off-policy evaluation techniques Understanding of calibration methods including isotonic regression and Platt scaling Experience with hierarchical, empirical-Bayes, or partial-pooling models Knowledge of constrained or multi-objective optimization approaches …