Data Scientist

• Modeling & ML Frameworks: Python, scikit-learn, PyTorch, TensorFlow — spanning classical ML, deep learning, and transformer-based architectures. Includes modern ensemble methods (XGBoost, LightGBM) for large-scale structured modeling.
• Applied Domains: Ranking, Recommendation, Dynamic Pricing, Forecasting, Supply–Demand Optimization, Semantic Search, NLP/NLU, Generative Content Systems
• Data & Compute: Databricks, PySpark, AWS (S3, Glue, EMR, Athena), ScyllaDB, MongoDB, Redis
• Experimentation & Optimization: MLflow, Airflow, SageMaker, Bayesian Optimization, Bandit/Sequential Experimentation
• LLMs & GenAI: Claude, OpenAI GPT-4, SLMs, LangChain, Cursor IDE, RAG Pipelines, Embedding Models, Vector Search (FAISS / Pinecone)
• Observability: Grafana, Prometheus, Data Quality Monitors, Custom Model Dashboards 

We’re in the early stages of building a Data Science & AI team — the learning curve, innovation velocity, and ownership opportunities are immense. You’ll help define the foundation for experimentation, production ML pipelines, and GenAI innovation from the ground up.

We’re hiring a Senior Data Scientist to build the next generation of intelligent decision systems that power pricing, supply optimization, ranking, and personalization in our global B2B hotel marketplace.

This is a high-impact role at the intersection of machine learning, optimization, and product engineering, where you’ll leverage deep statistical modeling and modern ML techniques to make real-time decisions at scale.

You’ll collaborate closely with Product, Engineering, and Data Platform teams to operationalize data science models that directly improve revenue, conversion, and marketplace efficiency.

You’ll own the full lifecycle of ML models—from experimentation and training to deployment, monitoring, and continuous retraining to ensure performance at scale.

Responsibilities:

● Design and implement ML models for dynamic pricing, availability prediction, and real-time hotel demand optimization.

● Develop and maintain data pipelines and feature stores supporting large-scale model training and inference. ● Leverage Bayesian inference, causal modeling, and reinforcement learning (bandits / sequential decision systems) to drive adaptive decision platforms.

● Build ranking/recommendation systems for personalization, relevance, and supply visibility.

● Use LLMs (Claude, GPT-4, SLMs) for:

○ Contract parsing, metadata extraction, and mapping resolution

○ Semantic search and retrieval-augmented generation (RAG)

○ Conversational systems for CRS, rate insights, and partner communication

○ Automated summarization and content enrichment

● Operationalize ML + LLM pipelines on Databricks / AWS for training, inference, and monitoring.

● Deploy and monitor models in production with strong observability, tracing, and SLO ownership.

● Run A/B experiments and causal validation to measure real business impact.

● Collaborate cross-functionally with engineering, data platform, and product teams to translate research into scalable production systems.

● Your models will directly influence GMV growth, conversion rates, and partner revenue yield across the global marketplace.

Requirements

● 5–9 years of hands-on experience in Applied ML / Data Science.

● Strong proficiency in Python, PySpark, and SQL.

● Experience developing models for ranking, pricing, recommendation, or forecasting at scale.

● Hands-on with PyTorch or TensorFlow for real-world ML or DL use cases.

● Strong grasp of probabilistic modeling, Bayesian methods, and causal inference.

● Practical experience integrating LLM/GenAI workflows (LangChain, RAG, embeddings, Claude, GPT, SLMs) into production.

● Experience with Databricks, Spark, or SageMaker for distributed training and deployment.

● Familiar with experiment platforms, MLflow, and model observability best practices.

● Strong business understanding and ability to communicate model impact to product stakeholders.

Nice to Have

● Background in travel-tech, marketplace, or pricing/revenue optimization domains.

● Experience in retrieval, semantic search, or content-based information retrieval.

● Familiarity with small language model (SLM) optimization for cost-efficient inference.

● Prior work on RL/bandit-driven decision systems or personalization engines.

● Experience designing AI-assisted developer workflows using tools like Cursor, Claude, or Code Interpreter.