Senior Data Scientist

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

📋 Description

• Construct and enhance censored bid-landscape models to estimate clearing-price distributions from auction data that is only partially observed.

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

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

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

• Create modeling approaches for look-alike audiences employing positive-unlabeled learning and embedding-based nearest-neighbor techniques.

• Execute advertiser-level calibration strategies while independently tracking ranking and calibration quality.

• Design effective offline evaluation frameworks that utilize inverse-propensity scoring, doubly-robust estimators, and importance reweighting.

• Establish exploration strategies and propensity logging techniques to ensure dependable downstream correction and evaluation.

• Develop constrained optimization mechanisms for campaign objectives, pricing limitations, and volume targeting.

• Assist with data diagnostics, capability evaluations, and evidence-driven model recommendations.

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

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

• Engage in architecture discussions and contribute to decisions regarding scalable ML platform design.

⛳️ Requirements

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

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

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

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

• In-depth understanding of regularization, calibration methods, and handling categorical features.

• Strong foundation in probability, statistics, confidence intervals, and power analysis.

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

• Practical hands-on experience with Spark or PySpark.

• Familiarity with experimentation frameworks and A/B testing methodologies.

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

• Knowledge of explainability techniques such as SHAP and permutation importance.

• Upper-Intermediate English proficiency or higher.

• Strong analytical and problem-solving capabilities.

• Ability to thrive in a highly data-driven environment.

• Excellent communication and stakeholder management skills.

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

• Proactive attitude with a strong sense of ownership.

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

🏝️ Benefits

• Employees have the option to work remotely.

• Opportunity to engage with technically challenging products.

• Collaboration with seasoned engineers and data scientists.

• Direct influence on large-scale production systems.