Senior Data Scientist

Job not on LinkedIn

🔥 20 hours ago

🌐 Germany, Poland – Remote

🇩🇪 Germany – Remote 🇵🇱 Poland – Remote

⏰ Full Time

🟠 Senior

📊 Data Scientist

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Sigma Software Group

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2002

💼 Consulting

🏥 Healthcare

🚘 Automotive

Consulting • Healthcare • Automotive

Sigma Software Group is a multinational company, established in 2002, that specializes in providing high-quality software development, graphic design, testing, and support services. The company focuses on delivering solutions across various industries such as automotive, telecommunications, aviation, advertising, gaming, banking, real estate, and healthcare. Sigma Software values professional growth, offers remote work opportunities worldwide, and caters to world-renowned clients like AstraZeneca, Scania, and SAS. The company emphasizes a culture of continuous education, mentorship, and flexible work environments, making it a preferred workplace for IT specialists aiming to work on complex solutions utilizing cutting-edge technologies. Sigma Software is committed to innovative solutions and engineering the future while also contributing to social causes such as charitable work in Ukraine.

📋 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 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

🎯 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 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 • Experience in AdTech modeling including CTR/CVR prediction, bid-landscape modeling, audience segmentation, and RTB mechanics is a plus • Experience working with sparse, delayed, or censored labels is a plus • Knowledge of attribution modeling, survival analysis, and positive-unlabeled learning is a plus • Practical experience with counterfactual and off-policy evaluation techniques is a plus • Understanding of isotonic regression and Platt scaling is a plus • Experience with hierarchical, empirical-Bayes, or partial-pooling models is a plus • Knowledge of constrained or multi-objective optimization approaches is a plus • Experience with uplift modeling and causal inference methods is a plus • Experience with Vertex AI or similar managed ML training environments is a plus • Publications, competitive modeling achievements, or open-source contributions related to Machine Learning or AdTech are a plus • Strong analytical and problem-solving skills • Ability to work effectively in a highly data-driven environment • Strong communication and stakeholder management abilities • Ability to explain complex modeling decisions to technical and non-technical audiences • Proactive mindset with strong ownership mentality • Attention to detail and scientific rigor in experimentation and evaluation

🏖️ Benefits

• Remote work availability • Opportunity to work on technically challenging products • Collaboration with experienced engineers and data scientists • Direct impact on large-scale production systems • Knowledge transfer and collaboration with the Customer’s internal data science team