Data Scientist

Tech Stack

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.

Role : Senior Data Scientist (AI & Data)

Location: Remote (Work from Home)

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.