Data Scientist - AMP

Aspire Software

We're looking for a Data Scientist who has taken machine learning models especially reinforcement learning from research to production. Today, our pricing engine is a rule-based parametric system (elasticity modeling, sigmoid demand curves, day-of-week weighting, occupancy and pickup deviation guardrails). Your job is to evolve it into a learning system: contextual bandits, RL policies, and probabilistic forecasting that price thousands of hotel-room-nights every day. You will also integrate other signals into this forecasted price, like competitor prices, events in the area, weather, etc.

You'll own this work end-to-end: framing the problem, designing rewards and offline evaluation, training models, and shipping them as production Python services on our FastAPI / AWS stack not handing notebooks to engineers. You'll be expected to move fast using AI-assisted development tools.

What You'll Work On

• Pricing Intelligence Replace and extend our parametric pricing engine (occupancy deviation, pickup velocity, price elasticity, booking curve forecasting, seasonality, day-of-week effects) with learned models: contextual bandits, RL policies, and Bayesian elasticity estimation

• RL in Production Design reward functions, exploration strategies, and off-policy evaluation that let us deploy RL pricing safely across multi-tenant hotel data; build the training, monitoring, and rollback infrastructure to support it

• Demand Forecasting Improve our booking-curve and final-occupancy forecasts (currently sigmoid-based) with proper time-series and probabilistic methods; quantify uncertainty and feed it into pricing decisions

• Simulation & Evaluation Extend our historical replay and synthetic simulation harness into a first-class offline evaluation and A/B testing framework for pricing policies

• LLM-Powered Features Build agentic workflows (OpenAI, Anthropic Claude, LangChain / LangGraph) for event-based pricing recommendations, demand analysis, and revenue-manager copilots

• Productionization Write production-grade Python services: typed, tested, modular packages running on FastAPI / SQLAlchemy / PostgreSQL the kind of code a staff engineer would approve, not scripts and notebooks thrown over the wall

• Data Pipelines Work with PredictHQ event data, competitor rate feeds, and PMS integrations (Seekda, InnQuest, others) to build reliable data flows that power pricing decisions

• Infrastructure Contribute to our AWS architecture (ECS Fargate, SQS, EventBridge, S3, CloudWatch) and help scale the platform as we grow

Tech Stack

• Core: Python 3.11, FastAPI, SQLAlchemy 2.0, Alembic, PostgreSQL, Redis

• ML / RL: PyTorch or TensorFlow, scikit-learn, Stable-Baselines3 / Ray RLlib (or equivalent), MLflow or similar experiment tracking

• AI / LLM: OpenAI GPT-4, Anthropic Claude, LangChain, LangGraph, PredictHQ

• Data: Pandas, Polars, NumPy, statsmodels

• Infrastructure: AWS (ECS Fargate, SQS, EventBridge, S3, CloudWatch, ECR), Docker, GitHub Actions CI/CD

• Observability: Prometheus, Grafana Loki, PostHog