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 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
