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 • Experience with uplift modeling and causal inference methods • Experience with Vertex AI or similar managed ML training environments • Publications, competitive modeling achievements, or open-source contributions related to Machine Learning or AdTech

PERSONAL PROFILE

• Strong analytical and problem-solving skills • Ability to work effectively in a highly data-driven environment • Strong communication and stakeholder management abilities • Ability to explain complex modeling decisions to technical and non-technical audiences • Proactive mindset with strong ownership mentality • Attention to detail and scientific rigor in experimentation and evaluation