At a glanceSummarised by Seekless from the posting.
Must have12
Experience taking machine-learning systems from problem definition through production deployment and monitoring
Strong Python and SQL skills, including experience building modular, typed, tested production software
Solid foundation in statistics, experimentation, forecasting, and model evaluation
Experience working with time-dependent, noisy, incomplete, or imperfect real-world data
Strong analytical problem-solving skills and a methodical approach to ambiguous technical issues
Proven ability to debug production systems, including data pipelines, APIs, asynchronous jobs, integrations, and model behavior
Experience investigating customer-reported issues, identifying root causes, and communicating findings clearly to technical and non-technical stakeholders
A habit of improving systems after incidents through better tests, monitoring, documentation, safeguards, or architecture
The judgment to balance model sophistication with reliability, explainability, and business value
Comfort working with APIs, relational databases, containers, cloud infrastructure, and CI/CD
Clear communication skills and an ability to work closely with engineers, product leaders, revenue-management experts, and customer-facing teams
High level of ownership: turn ambiguous business or customer problem into practical, measurable plan and carry it through to completion
Nice to have9
Contextual bandits, reinforcement learning, recommendation systems, bidding, or other sequential decision systems
Probabilistic demand forecasting or hierarchical Bayesian modeling
Pricing, revenue management, marketplaces, travel, hospitality, advertising, or e-commerce
Causal inference and off-policy evaluation
Model drift, data-quality monitoring, and experiment-tracking systems
Multi-tenant SaaS or event-driven architectures
Debugging distributed systems and third-party integrations in production
LLM applications and agentic workflows
AWS services such as ECS, SQS, EventBridge, S3, and CloudWatch
Skills
Python
SQL
FastAPI
Pydantic
SQLAlchemy
Alembic
PostgreSQL
Supabase
Redis
Pandas
Polars
NumPy
statsmodels
OpenAI
Anthropic
LangChain
LangGraph
AWS ECS
Fargate
SQS
EventBridge
S3
CloudWatch
Docker
GitHub Actions
Prometheus
PostHog
PredictHQ
About the company
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 role
About the job:
We are looking for a Senior Machine Learning Engineer who wants to own that evolution.
This is not a research-only role. You will frame problems, explore data, design models, build evaluation systems, investigate real customer issues, and ship production services. Your work will influence real pricing decisions and measurable hotel revenue not just dashboards or internal prototypes.
Responsibilities
What You’ll Work On
•
-Pricing intelligence: Advance our pricing engine using probabilistic forecasting, elasticity estimation, contextual bandits, reinforcement learning, and other appropriate optimization methods.
•
Demand forecasting: Improve booking-curve and final-occupancy forecasts, including uncertainty estimates that can inform safer pricing decisions.
•
Safe experimentation: Build historical replay, simulation, backtesting, shadow deployment, and controlled rollout capabilities to measure revenue impact before models affect customers.
•
Production ML: Turn models into reliable, tested, and observable Python services, owning the path from experimentation through deployment, monitoring, and rollback.
•
Problem-solving and customer investigations: Debug unexpected pricing, data, integration, and production behavior. Trace customer-reported issues to their root cause and turn incidents into lasting improvements.
•
Data and intelligent experiences: Strengthen the reservation, occupancy, competitor-rate, PMS, and event-data pipelines behind our decisions, while contributing to AI-powered explanations and revenue-manager tools.
Tech Stack
•
Python 3.11, FastAPI, Pydantic, SQLAlchemy 2.0, and Alembic
•
PostgreSQL/Supabase and Redis
•
Pandas, Polars, NumPy, and statsmodels
•
OpenAI, Anthropic, LangChain, and LangGraph
•
PredictHQ and multiple hotel PMS and market-data integrations
•
AWS ECS/Fargate, SQS, EventBridge, S3, and CloudWatch
•
Docker, GitHub Actions, Prometheus, and PostHog
Requirements
•
Experience taking machine-learning systems from problem definition through production deployment and monitoring.
•
Strong Python and SQL skills, including experience building modular, typed, tested production software—not only exploratory notebooks.
•
A solid foundation in statistics, experimentation, forecasting, and model evaluation.
•
Experience working with time-dependent, noisy, incomplete, or imperfect real-world data.
•
Strong analytical problem-solving skills and a methodical approach to ambiguous technical issues.
•
Proven ability to debug production systems, including data pipelines, APIs, asynchronous jobs, integrations, and model behavior.
•
Experience investigating customer-reported issues, identifying root causes, and communicating findings clearly to technical and non-technical stakeholders.
•
A habit of improving systems after incidents through better tests, monitoring, documentation, safeguards, or architecture.
•
The judgment to balance model sophistication with reliability, explainability, and business value.
•
Comfort working with APIs, relational databases, containers, cloud infrastructure, and CI/CD.
•
Clear communication skills and an ability to work closely with engineers, product leaders, revenue-management experts, and customer-facing teams.
•
A high level of ownership: you can turn an ambiguous business or customer problem into a practical, measurable plan and carry it through to completion.
•
Years of experience are a useful signal, but not the deciding factor. We care more about what you have built, how you reason, and how you respond when models and systems meet the real world.
Nice to Have
You do not need everything on this list:
•
Contextual bandits, reinforcement learning, recommendation systems, bidding, or other sequential decision systems.
•
Probabilistic demand forecasting or hierarchical Bayesian modeling.
•
Pricing, revenue management, marketplaces, travel, hospitality, advertising, or e-commerce.
•
Causal inference and off-policy evaluation.
•
Model drift, data-quality monitoring, and experiment-tracking systems.
•
Multi-tenant SaaS or event-driven architectures.
•
Debugging distributed systems and third-party integrations in production.
•
LLM applications and agentic workflows.
•
AWS services such as ECS, SQS, EventBridge, S3, and CloudWatch.
•
Hospitality experience is welcome, but not required. We will help you learn the domain.
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