Data Scientist - AMP
Core
Evolve a rule-based hotel pricing engine into a production reinforcement learning system that prices thousands of room-nights daily using contextual bandits, RL policies, and probabilistic forecasting.
Role type
Senior IC machine-learning engineer (reinforcement learning & pricing)
Builds
Production pricing services, demand forecasting models, and LLM-powered revenue management copilots for multi-tenant hotel platforms
Domain
Hospitality technology / Revenue management / Dynamic pricing
Deliverable
production ML models
Required skills
Reinforcement learning (contextual bandits, RL policies), Python (production-grade, typed, modular), FastAPI, AWS (ECS Fargate, SQS, EventBridge), PyTorch/TensorFlow, SQL/PostgreSQL, LLM frameworks (LangChain, LangGraph), time-series forecasting, Bayesian methods, causal inference, off-policy evaluation, reward design, CI/CD, Docker
Preferred skills
Revenue management domain expertise, price elasticity estimation, event-driven architectures, model observability, multi-tenant SaaS experience
Technologies
Python 3.11, FastAPI, SQLAlchemy, PostgreSQL, Redis, PyTorch, TensorFlow, Stable-Baselines3, Ray RLlib, MLflow, OpenAI, Anthropic Claude, LangChain, LangGraph, PredictHQ, Pandas, Polars, NumPy, statsmodels, AWS (ECS Fargate, SQS, EventBridge, S3, CloudWatch, ECR), Docker, GitHub Actions, Prometheus, Grafana Loki, PostHog
Responsibilities
Design reward functions and exploration strategies for RL pricing systems; build training, monitoring, and rollback infrastructure for RL deployment; improve booking-curve and occupancy forecasts with probabilistic time-series methods; extend historical replay harnesses into offline evaluation and A/B testing frameworks; build agentic workflows for event-based pricing recommendations; write production-grade Python services replacing notebooks; integrate external data feeds (competitor rates, events, weather) into pricing decisions; contribute to AWS architecture scaling
Seniority
Senior, hands-on IC
