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

Requirements

*   4+ years of professional data science / ML engineering experience with models running in production (not just notebooks, dashboards, or analytics)

*   Production reinforcement learning experience — you have personally designed, trained, deployed, and monitored at least one RL or contextual-bandit system serving real users at scale. You can speak in detail to:reward design, exploration / exploitation trade-offs, off-policy evaluation, distribution shift, safe rollout, and what broke when the model met production

*   Strong Python development skills beyond scripting and Jupyter— you write modular, typed, tested Python packages;you're comfortable with async patterns, ORMs (SQLAlchemy), building production APIs (FastAPI or similar), and you can hold your own in a code review with backend engineers

*   Solid foundations in classical ML, statistics, and time-series — regression, Bayesian methods, causal inference, demand forecasting, price elasticity

*   Experience working with LLMs (OpenAI, Anthropic, or similar) and frameworks like LangChain or LangGraph for agentic workflows

*   AI-assisted development is a must— you actively use tools like Claude Code, Cursor, GitHub Copilot, or similar to accelerate your workflow. We expect you to ship faster and think bigger because of these tools

*   Strong SQL and data-modeling skills (PostgreSQL preferred)

*   Experience with AWS cloud services or equivalent cloud platforms

*   Comfortable working with Docker, CI/CD pipelines, and production deployments

Nice to Have

*   Experience in revenue management, hospitality tech, dynamic pricing, yield optimization, or ad / e-commerce bidding

*   Background in price elasticity estimation, contextual bandits for pricing or recommendation, or hierarchical Bayesian demand models

*   Experience with event-driven architectures (SQS,EventBridge, or similar)

*   Familiarity with model and data observability — Prometheus / Grafana, drift detection, model performance dashboards

*   Experience building multi-tenant SaaS platforms

*   Publications, open-source contributions, or competition results in ML / RL

What We Value

*   Speed with quality— Ship fast, but ship code and models a staff engineer would approve

*   AI-native workflow— You don't just know about AI tools, you use them daily to write, debug, and architect

*   Ownership— Pick up a problem and drive it to completion without hand-holding

*   Simplicity— Elegant solutions over over-engineered ones. Minimal code that does the job

*   Curiosity— Our domain (hotel revenue optimization) has real depth.You're excited to learn it