Machine learning and data science sit at the heart of Jabama's mission to make travel smarter, more personal, and more efficient. We build systems that understand user intent, forecast demand, optimize pricing, and connect travelers to the right experience at the right moment.

As a Senior Data Scientist, you'll own problems end to end — from framing an ambiguous business question to shipping the model that answers it and keeping it healthy in production. This role sits at the intersection of rigorous statistical thinking and solid software engineering: we're looking for someone who cares as much about whether a model is correct as whether it's maintainable.

You'll join a chapter that values ownership, technical depth, and clear reasoning over volume of models shipped.

What You'll Be Doing

• Own data science problems across the full ML lifecycle — problem framing, data modeling, experimentation, deployment, and monitoring
• Build and maintain predictive and inferential models for forecasting, anomaly detection, regression, classification, and ranking
• Apply sound statistical reasoning and experimental design so that our models are interpretable, robust, and trustworthy
• Design and optimize data and ML pipelines using tools such as Spark, Airflow, and modern ML frameworks
• Build monitoring and visibility tooling that tracks model health, data drift, and business impact
• Write clean, production-ready code and reusable components that hold up to engineering review
• Partner with data engineers, product managers, and software engineers to turn business objectives into measurable outcomes
• Raise the bar on data quality, model explainability, and system reliability across the chapter
• Keep an eye on developments in ML and large-scale systems, and bring the useful ones back to the team

What We're Looking For

• A Master's degree in Computer Science, AI, statistics, or another quantitative field
• 3+ years applying data science and machine learning in production environments, with a track record of models that shipped and stayed shipped
• Strong grounding in statistics, probability, and mathematical modeling — you can explain why a method works, not just call it
• Solid command of ML fundamentals and hands-on experience across several families of methods (statistical models, tree-based methods, neural networks)
• Demonstrated experience with real-world applications such as forecasting, classification, regression, or anomaly detection
• Excellent Python and SQL, backed by genuine software engineering instincts — testing, version control, code review, sensible abstractions
• Experience building scalable pipelines and production systems with Spark, Airflow, or comparable tooling
• Working familiarity with transactional and analytical databases (e.g. PostgreSQL, ClickHouse)
• Experience building monitoring or visualization tooling that turns technical performance into decisions people can act on
• Strong communication skills and the ability to drive alignment between technical and business stakeholders
• A proactive mindset, real sense of ownership, and a passion for technical excellence