Data Scientist, Engineer

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Minor Hotels Europe and Americas

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1978

👥 B2C

💰 Post-IPO Equity on 2018-05

B2C • Hospitality • Travel

Minor Hotels Europe and Americas is a hospitality company that operates a diverse portfolio of hotels and resorts across Europe and the Americas. The company focuses on delivering exceptional guest experiences through high-quality service and unique accommodations, catering to both leisure and business travelers.

📋 Description

• Analyze high-dimensional sensor and feature datasets using UMAP, t-SNE, PCA, and similar techniques • Identify clusters, anomalies, blind spots, distribution gaps, and class or environment mismatches • Diagnose dataset shift, domain drift, sparsity, and representation collapse • Perform data analysis aligned with classical ML models including XGBoost, SVR, k-NN, and tree-based models • Support analysis for deep learning models such as CNNs and Transformers • Analyze embeddings, confusion matrices, and model failure patterns to trace errors back to data issues • Investigate imbalanced data, noisy sensor signals, mislabeled samples, and ambiguous cases • Develop approaches for improving weakly labeled or unlabeled data including clustering and pseudo-labeling • Perform data mining on large collections of field data to extract insights and patterns • Design processes for converting noisy or partially verified data into high-quality validated datasets • Translate exploratory findings into clear recommendations for data filtering, relabeling, or new data collection • Advocate for and implement data-centric improvements to enhance model robustness • Work closely with engineering teams to integrate improved data workflows into ML pipelines

🎯 Requirements

• Master's or PhD degree in Data Science, Computer Science, Applied Mathematics, Statistics, Physics, or a related field • 2+ years of hands-on experience working with machine learning datasets • Experience with time-series, sensor, image, or video data • Strong Python skills and experience with NumPy, pandas, matplotlib, and seaborn • Experience with dimensionality reduction and representation analysis techniques such as UMAP, t-SNE, and PCA • Solid understanding of machine learning fundamentals, model evaluation, and diagnostics • Experience supporting both traditional machine learning and deep learning projects • Nice to Have: Experience working with sensor data, including radar, magnetic, environmental, 3D, or IoT datasets • Familiarity with scikit-learn preprocessing workflows • Experience handling imbalanced datasets, noisy labels, sensor noise, and data drift • Knowledge of model interpretability, feature importance, and embedding analysis • Experience working with data annotation and labeling teams • Familiarity with MLflow, Weights & Biases, DVC, or similar tools

🏖️ Benefits

• health insurance from the first days • Christmas holidays from December 25 to December 31 • Cooperation with Superhumans center and Veteran HUB