Senior Data Scientist

New

Sigma SoftwareProgrammatic advertising

Workplace type: remote; Locations: Warszawa, Chmielna 73, Warszawa, Country code: PLFull-TimeSenior

Salary not disclosed

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Job Details

Languages: Upper-Intermediate English level or higher
Experience: 5+ years of experience in Machine Learning or Data Science
Required Skills: PythonSQLMachine LearningNumpyData sciencePandasscikit-learn

Have 5+ years of experience in Machine Learning or Data Science with production-grade models measured against business KPIs.
Have strong Python skills, including numpy, pandas, and scikit-learn.
Have strong SQL skills and experience working with large-scale datasets.
Have practical experience with XGBoost, LightGBM, or CatBoost.
Understand regularization, calibration methods, and categorical feature handling.
Have strong knowledge of probability, statistics, confidence intervals, and statistical power analysis.
Have experience with feature engineering for structured and behavioral datasets.
Have hands-on experience with Spark or PySpark.
Have practical knowledge of experimentation frameworks and A/B testing methodologies.
Have experience with temporal splits, leakage detection, drift analysis, and slice-based metrics.
Understand explainability techniques such as SHAP and permutation importance.
Have upper-intermediate English or higher.

Build censored bid-landscape models to estimate clearing-price distributions from partially observed auction data.
Develop real-time win probability, conversion propensity, lift estimation, and look-alike audience models.
Implement advertiser-level calibration strategies and monitor ranking and calibration quality.
Design offline evaluation frameworks using inverse-propensity scoring, doubly-robust estimators, and importance reweighting.
Define exploration strategies and propensity logging approaches for 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 contribute to scalable ML platform design.

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