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 Customers 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

Remote Work :

Yes

Employment Type :

Full-time