About the role
Job Description
* 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
Qualifications
* 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
Additional Information
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