Data Scientist - AMP

Aspire Software is looking for a Data Scientist to join our team in Lebanon.

Here is a little window into our company: Aspire Software operates and manages wholly owned software companies, providing mission-critical solutions across multiple verticals. By implementing industry best practices, Aspire delivers a time sensitive integration process, and the operation of a decentralized model has allowed it to become a hub for creating rapid growth by reinvesting in its portfolio.

About the job:

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