Senior Data Scientist (Availability)

• End-to-End ML Ownership: Lead ML projects from formulating hypotheses and task setting to delivering measurable business impact. You will be responsible for the entire cycle, ensuring fast time-to-market and maintaining model quality in production.
• Cross-functional Collaboration: Work closely with Data Engineers to build robust production data pipelines, and with Data Analysts to dive into business context, define metrics, and design/evaluate A/B tests.
• Domain-Specific Problem Solving: Translate business goals into mathematical models. You will solve core domain challenges, such as: Rate Matching (deduplication of rates from 350+ suppliers), Alternative Search (RecSys & Ranking for unavailable rates), Multi-objective Optimization (balancing margin maximization with incident minimization), and Cache Freshness Prediction (predicting the likelihood of a rate changing before booking).
• Model Development: Develop, train, and validate machine learning models to test product hypotheses and proactively improve the B2B user experience.

End-to-End ML Ownership: Lead ML projects from formulating hypotheses and task setting to delivering measurable business impact. You will be responsible for the entire cycle, ensuring fast time-to-market and maintaining model quality in production.Cros...

Must Have:

• Experience: At least 4+ years of hands-on experience as a Data Scientist or ML Engineer.
• Business Mindset: Ability to dig into data, identify root business problems, and translate business objectives into clear ML tasks rather than just tuning metrics in a vacuum.
• ML Stack & Algorithms: Solid knowledge of classic Machine Learning (Gradient Boosting like CatBoost/LightGBM, Classification, Regression).
• Matching, RecSys & Ranking: Proven experience with entity matching tasks, and a deep understanding of Recommendation Systems and Ranking approaches (KNN, FAISS, Learning-to-Rank, pointwise / pairwise / listwise approaches).
• Big Data Stack: Confident experience working with massive datasets (we process terabytes daily). Excellent SQL skills and practical experience building pipelines with PySpark.
• Engineering Independence: Production-quality Python code, ability to write tests, and readiness to bring models to production (experience with Airflow and Python microservices).

Nice to Have:

• Industry Experience: Experience in TravelTech, E-commerce, or B2B APIs (understanding of hotels, rates, distributors).
• NLP & Embeddings: Basic NLP skills, understanding the principles of text embeddings and how to train them (highly useful for matching textual rate descriptions, room types, and cancellation policies).
• Optimization & Pricing: Experience with dynamic pricing, multi-objective optimization, or Next Best Action (NBA) systems.
• Math & Stats: A solid foundation in probability theory and mathematical statistics for rigorous experiment design and model evaluation.

• End-to-End ML Ownership: Lead ML projects from formulating hypotheses and task setting to delivering measurable business impact. You will be responsible for the entire cycle, ensuring fast time-to-market and maintaining model quality in production.
• Cross-functional Collaboration: Work closely with Data Engineers to build robust production data pipelines, and with Data Analysts to dive into business context, define metrics, and design/evaluate A/B tests.
• Domain-Specific Problem Solving: Translate business goals into mathematical models. You will solve core domain challenges, such as: Rate Matching (deduplication of rates from 350+ suppliers), Alternative Search (RecSys & Ranking for unavailable rates), Multi-objective Optimization (balancing margin maximization with incident minimization), and Cache Freshness Prediction (predicting the likelihood of a rate changing before booking).
• Model Development: Develop, train, and validate machine learning models to test product hypotheses and proactively improve the B2B user experience.

End-to-End ML Ownership: Lead ML projects from formulating hypotheses and task setting to delivering measurable business impact. You will be responsible for the entire cycle, ensuring fast time-to-market and maintaining model quality in production.Cros...

Must Have:

• Experience: At least 4+ years of hands-on experience as a Data Scientist or ML Engineer.
• Business Mindset: Ability to dig into data, identify root business problems, and translate business objectives into clear ML tasks rather than just tuning metrics in a vacuum.
• ML Stack & Algorithms: Solid knowledge of classic Machine Learning (Gradient Boosting like CatBoost/LightGBM, Classification, Regression).
• Matching, RecSys & Ranking: Proven experience with entity matching tasks, and a deep understanding of Recommendation Systems and Ranking approaches (KNN, FAISS, Learning-to-Rank, pointwise / pairwise / listwise approaches).
• Big Data Stack: Confident experience working with massive datasets (we process terabytes daily). Excellent SQL skills and practical experience building pipelines with PySpark.
• Engineering Independence: Production-quality Python code, ability to write tests, and readiness to bring models to production (experience with Airflow and Python microservices).

Nice to Have:

• Industry Experience: Experience in TravelTech, E-commerce, or B2B APIs (understanding of hotels, rates, distributors).
• NLP & Embeddings: Basic NLP skills, understanding the principles of text embeddings and how to train them (highly useful for matching textual rate descriptions, room types, and cancellation policies).
• Optimization & Pricing: Experience with dynamic pricing, multi-objective optimization, or Next Best Action (NBA) systems.
• Math & Stats: A solid foundation in probability theory and mathematical statistics for rigorous experiment design and model evaluation.