Senior Data Scientist (Look-2-Book)

RateHawk; United States; Hybrid; USD 120,000 - 190,000; Full time

Benefits offered by this job

Flexible work schedule

Remote work options

Professional development support

Travel discounts

Job summary

ETG seeks an experienced Data Scientist/ML Engineer to own the full ML lifecycle, delivering business impact and fast time-to-market. You will partner with data engineers and analysts to build robust data pipelines, define metrics, and optimize the look-to-book and pricing workflows.

Strong SQL, PySpark, and production-grade Python skills are required. Nice-to-have experience in TravelTech or B2B APIs, NLP embeddings, and multi-objective optimization, plus a flexible work arrangement including

Qualifications

• 4+ years hands-on experience as a Data Scientist or ML Engineer.
• Ability to translate business objectives into ML tasks.
• Solid knowledge of classical ML methods (gradient boosting, classification, regression).
• Proven experience with entity matching and recommender systems.
• Experience with terabytes-scale data and SQL; PySpark pipelines.
• Production-quality Python, testing, and microservices (Airflow).

Responsibilities

• Lead ML projects from hypothesis to measurable business impact.
• Collaborate with data engineers and data analysts to build pipelines.
• Translate travel/B2B problems into ML tasks and models.
• Develop, train, and validate models to improve UX and outcomes.

Skills

End-to-End ML

Cross-functional collaboration

ML stack knowledge

Recommendation systems

Big data processing

Production code

Airflow

Python

PySpark

Model evaluation

Education

4+ years in data science or ML

Tools

CatBoost

LightGBM

KNN

FAISS

Job description

• 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 complex travel/B2B business problems into mathematical and ML modeling tasks. Key challenges include: Look-to-Book Optimization: Design and deploy scoring models to accurately predict the probability of a search query converting into a booking (look-to-book), model how this likelihood changes dynamically with price and availability, and optimize the entire search-to-book funnel / Multi-objective Optimization: Simultaneous optimization of margin/profitability and operational incident minimization; sophisticated trade-off recommendations factoring in B2B client preferences / Cache Freshness Prediction: Predict the likelihood of rates/offers becoming stale before booking, helping find the right balance between data accuracy, speed, and system load.
• Model Development: Develop, train, and validate machine learning models to test product hypotheses and proactively improve the B2B user experience.

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.
• A fully flexible work schedule — there’s no pressure to start work at exactly 9:00 AM; what matters is achieving results and moving forward;
• Each person in our team is encouraged to choose their preferred work format. You can work fully remotely, come to the office, or choose a hybrid work model;
• We are an ambitious and supportive team who love what they do, appreciate each other, and grow together;
• The growth and development of each employee is our priority, so we have internal programs available for adaptation and training, development of soft skills and leadership abilities that are tailored individually to each employee;
• We also provide partial compensation for employees participating in external training and conferences;
• In tourism, it’s difficult to grow without an excellent knowledge of English, and we support our employees’ language learning goals — we organize group and individual lessons, plus speaking clubs with colleagues from all over the world;
• And, of course, to encourage you to travel more, we offer corporate prices on hotels and other travel services;
• We prioritize well-being and are committed to supporting the overall health and work-life balance at ETG. As part of this commitment, we provide MyTime Day Off - an extra day off that is designed to give our employees the flexibility to focus on important matters, whether it’s taking care of their health, mental recharge, addressing personal issues, or any other important activities.

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