Senior Data Scientist (Search)

The Hotel Search Team develops core components that are critical for the company’s business: hotel ranking in search results, search suggestions, and improving conversion to hotel page visits.

• Manage end-to-end ML projects: problem definition → solution → testing → production → support.
• Work with data engineers to build datasets and define data requirements, assess feasibility, risks, and limitations.
• Work with data analysts to design and analyze A/B tests: metrics, splits, result interpretation, and rollout recommendations.
• Develop and train models (classical ML + DL).
• Deploy models and code to production (Python services), support releases and integrations.
• Be responsible for post-launch model quality: metrics, monitoring, drift/degradation tracking, improvement plans, and support processes.

The Hotel Search Team develops core components that are critical for the company’s business: hotel ranking in search results, search suggestions, and improving conversion to hotel page visits.Manage end-to-end ML projects: problem definition → solution...

• Experience managing end-to-end ML projects in production (from problem definition to support).
• Strong understanding of classical ML: feature engineering, boosting algorithms, classification/regression, cross-validation, threshold tuning, and calibration.
• Experience working with search or recommendation systems.
• Experience with DL (PyTorch/TensorFlow): understanding of fine-tuning principles and model inference.
• Python (production-grade): readable code, tests for critical components, understanding of model/artifact packaging and service integration.
• Understanding of ML monitoring: quality metrics, drift, alerts, diagnostics, and support processes.
• SQL proficiency sufficient for independent dataset building (joins, window functions).
• Experience with model interpretability and error analysis.
• MLflow / W&B / DVC or similar experiment tracking tools.
• Orchestration/pipelines (Airflow/Prefect/Dagster) and advanced data processing.
• Conversational English.

Nice to have:

• Experience with neural networks in the context of vector search, ranking, NLP, or CV;
• Experience working with Big Data technologies: Hadoop, Spark.

The Hotel Search Team develops core components that are critical for the company’s business: hotel ranking in search results, search suggestions, and improving conversion to hotel page visits.

• Manage end-to-end ML projects: problem definition → solution → testing → production → support.
• Work with data engineers to build datasets and define data requirements, assess feasibility, risks, and limitations.
• Work with data analysts to design and analyze A/B tests: metrics, splits, result interpretation, and rollout recommendations.
• Develop and train models (classical ML + DL).
• Deploy models and code to production (Python services), support releases and integrations.
• Be responsible for post-launch model quality: metrics, monitoring, drift/degradation tracking, improvement plans, and support processes.

The Hotel Search Team develops core components that are critical for the company’s business: hotel ranking in search results, search suggestions, and improving conversion to hotel page visits.Manage end-to-end ML projects: problem definition → solution...

• Experience managing end-to-end ML projects in production (from problem definition to support).
• Strong understanding of classical ML: feature engineering, boosting algorithms, classification/regression, cross-validation, threshold tuning, and calibration.
• Experience working with search or recommendation systems.
• Experience with DL (PyTorch/TensorFlow): understanding of fine-tuning principles and model inference.
• Python (production-grade): readable code, tests for critical components, understanding of model/artifact packaging and service integration.
• Understanding of ML monitoring: quality metrics, drift, alerts, diagnostics, and support processes.
• SQL proficiency sufficient for independent dataset building (joins, window functions).
• Experience with model interpretability and error analysis.
• MLflow / W&B / DVC or similar experiment tracking tools.
• Orchestration/pipelines (Airflow/Prefect/Dagster) and advanced data processing.
• Conversational English.

Nice to have:

• Experience with neural networks in the context of vector search, ranking, NLP, or CV;
• Experience working with Big Data technologies: Hadoop, Spark.