Senior Data Scientist

Company Description Join Sigma Software to help build advanced machine learning solutions for one of the large-scale players in the programmatic advertising ecosystem. We are looking for a Senior Machine Learning Engineer with strong production ML expertise and deep interest in real-time optimization systems, large-scale behavioral data, and AdTech challenges. In this role, you will work with a dedicated Sigma Software team on a predictive modeling platform integrated with a live ad exchange processing hundreds of millions of auction requests daily. You will contribute to sophisticated ML solutions involving bid optimization, calibration, counterfactual evaluation, and constrained decision-making systems. We as a company offer the opportunity to work on technically challenging products, collaborate with experienced engineers and data scientists, and make a direct impact on large-scale production systems. CUSTOMER Our Customer is a technology company operating supply-side infrastructure within the programmatic advertising ecosystem. The company manages a high-scale ad exchange platform and is investing in predictive decisioning capabilities to improve advertising performance, audience targeting, and campaign optimization through advanced machine learning technologies. PROJECT The project focuses on building a predictive modeling and optimization platform on top of a live ad exchange environment. The platform evaluates and filters advertising supply in real time, predicts high-performing audience contexts, builds look-alike audiences from small seed datasets, and optimizes campaign performance across multiple business objectives and operational constraints. The team works on complex machine learning challenges including censored bid-landscape modeling, sparse and delayed conversion attribution, calibration systems, counterfactual evaluation, and constrained optimization models. The solution is designed for large-scale production use and close collaboration with the Customer’s internal data science organization. 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 dynamicsDesign and implement hierarchical lift estimation models with confidence-bound-based selection strategiesBuild conversion propensity models using sparse, delayed, and aggregate-only labelsDevelop look-alike audience modeling approaches using positive-unlabeled learning and embedding-based nearest-neighbor techniquesImplement advertiser-level calibration strategies while independently monitoring ranking and calibration qualityDesign robust offline evaluation frameworks using inverse-propensity scoring, doubly-robust estimators, and importance reweightingDefine exploration strategies and propensity logging approaches to ensure reliable downstream correction and evaluationDevelop constrained optimization mechanisms for campaign objectives, pricing constraints, and volume targetingContribute to data diagnostics, capability assessments, and evidence-based model recommendationsCollaborate with the Customer team during post-launch tuning and performance validation cyclesPrepare technical documentation and knowledge transfer materials for the Customer’s internal data science teamParticipate 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 KPIsStrong Python skills including numpy, pandas, and scikit-learnStrong SQL skills and experience working with large-scale datasetsDeep practical experience with XGBoost, LightGBM, or CatBoostStrong understanding of regularization, calibration methods, and categorical feature handlingStrong knowledge of probability, statistics, confidence intervals, and statistical power analysisExperience with feature engineering for structured and behavioral datasetsHands-on experience with Spark or PySparkPractical knowledge of experimentation frameworks and A/B testing methodologiesExperience with advanced validation approaches including temporal splits, leakage detection, drift analysis, and slice-based metricsUnderstanding of explainability techniques such as SHAP and permutation importanceUpper-Intermediate English level or higher WILL BE A PLUS Experience in AdTech modeling including CTR/CVR prediction, bid-landscape modeling, audience segmentation, and RTB mechanicsExperience working with sparse, delayed, or censored labelsKnowledge of attribution modeling, survival analysis, and positive-unlabeled learningPractical experience with counterfactual and off-policy evaluation techniquesUnderstanding of calibration methods including isotonic regression and Platt scalingExperience with hierarchical, empirical-Bayes, or partial-pooling modelsKnowledge of constrained or multi-objective optimization approachesExperience with uplift modeling and causal inference methodsExperience with Vertex AI or similar managed ML training environmentsPublications, competitive modeling achievements, or open-source contributions related to Machine Learning or AdTech Additional Information PERSONAL PROFILE Strong analytical and problem-solving skillsAbility to work effectively in a highly data-driven environmentStrong communication and stakeholder management abilitiesAbility to explain complex modeling decisions to technical and non-technical audiencesProactive mindset with strong ownership mentalityAttention to detail and scientific rigor in experimentation and evaluation