Senior Data Scientist

Stack

Responsibilities

• ▹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 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 for 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
• ▹Prepare technical documentation and knowledge-transfer materials for the customer's internal data science team
• ▹Participate in architecture discussions and scalable ML platform design decisions

Requirements

• ▹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

Nice to have

• ▹Experience in AdTech modeling: CTR/CVR prediction, bid-landscape modeling, audience segmentation, 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

Soft skills