Senior Data Scientist

Sigma Software

10d

About The Position

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.

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
Upper-Intermediate English level or higher

Nice To Haves

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

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 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