Senior Data Scientist

This is a fully remote position, open to applicants in Germany, +1 more country.

📋 Description

• Develop and enhance censored bid-landscape models to forecast clearing-price distributions from incomplete auction data.

• Create real-time win probability estimation models that adapt to bid pricing fluctuations.

• Design and execute hierarchical lift estimation models utilizing confidence-bound selection strategies.

• Construct conversion propensity models leveraging sparse, delayed, and aggregate-only labels.

• Innovate look-alike audience modeling techniques through positive-unlabeled learning and embedding-based nearest-neighbor methods.

• Apply advertiser-level calibration strategies while independently assessing ranking and calibration quality.

• Develop comprehensive offline evaluation frameworks using inverse-propensity scoring, doubly-robust estimators, and importance reweighting.

• Establish exploration strategies and propensity logging methods to guarantee reliable downstream correction and evaluation.

• Create constrained optimization frameworks for campaign objectives, pricing limitations, and volume targeting.

• Contribute to data diagnostics, capability evaluations, and evidence-based model recommendations.

• Collaborate with the Customer team during post-launch tuning and performance validation phases.

• Prepare technical documentation and knowledge transfer resources for the Customer's internal data science team.

• Engage in architecture discussions and influence the design of scalable ML platform decisions.

⛳️ Requirements

• Over 5 years of experience in Machine Learning or Data Science with production-level models evaluated against business KPIs.

• Proficient in Python, including libraries such as numpy, pandas, and scikit-learn.

• Strong SQL capabilities and experience with large-scale datasets.

• Extensive practical experience with XGBoost, LightGBM, or CatBoost.

• Solid understanding of regularization, calibration techniques, and handling categorical features.

• Comprehensive knowledge of probability, statistics, confidence intervals, and statistical power analysis.

• Experience in feature engineering for both structured and behavioral datasets.

• Hands-on expertise with Spark or PySpark.

• Practical knowledge of experimentation frameworks and A/B testing methodologies.

• Familiarity with advanced validation techniques including temporal splits, leakage detection, drift analysis, and slice-based metrics.

• Understanding of explainability methods such as SHAP and permutation importance.

• Proficient in English at an upper-intermediate level or higher.

• Strong analytical and problem-solving capabilities.

• Ability to perform effectively in a data-driven setting.

• Excellent communication and stakeholder management skills.

• Capacity to articulate complex modeling decisions to both technical and non-technical audiences.

• Proactive attitude with a strong sense of ownership.

• Meticulous attention to detail and scientific rigor in experimentation and evaluation.

🏝️ Benefits

• Employees have the flexibility to work remotely.

• Opportunity to engage with technically challenging products.

• Collaboration with seasoned engineers and data scientists.

• Direct influence on large-scale production systems.