Senior Machine Learning Scientist – Applied

Position: Senior Machine Learning Scientist – Applied Payments
About the Role

Expedia Group brands power global travel for everyone, everywhere. We design cutting‑edge tech to make travel smoother and more memorable, and we create groundbreaking solutions for our partners. Our diverse, vibrant, and welcoming community is essential in driving our success.

Why Join Us?

To shape the future of travel, people must come first. Guided by our Values and Leadership Agreements, we foster an open culture where everyone belongs, differences are celebrated and we know that when one of us wins, we all win.

We provide a full benefits package, including travel perks, generous time‑off, parental leave, a flexible work model (with some pretty cool offices), and career development resources, all to fuel our employees' passion for travel and ensure a rewarding career journey. We’re building a more open world. Join us.

The Technology Team at Expedia Group partners with our Product teams to create innovative products, services, and tools to deliver high‑quality experiences for travelers, partners, and our employees. A singular technology platform powered by data and machine learning provides secure, differentiated, and personalized experiences that drive loyalty and traveler satisfaction.

Introduction to the Team:

Payments at Expedia Group sits at the intersection of trust, conversion, and global scale. Every millisecond counts and every decision impacts traveler experience, authorization rates, cost to serve, and platform reliability. As our Senior Machine Learning Scientist for Payments, you’ll be the technical lead bringing practical, high‑impact ML to a complex, high‑volume domain—turning ambiguous business problems into scalable solutions that quietly power millions of secure transactions worldwide.

If you’re excited by ownership, product thinking, and making ML work in production (not just on paper), this is where you’ll have outsized impact.

Responsibilities

• Lead ML for Payments:
Serve as the technical lead for a small, focused ML group supporting Payments. Set the roadmap, shape best practices, and mentor 1–2 scientists/engineers in the area.

• Partner deeply with Product & Payments Engineering:
Co‑define problems, discover hidden ML opportunities, and align on KPIs (e.g., authorization and approval rates, false decline reduction, latency/SLA adherence, cost optimization, partner routing quality).

• Ship production models end‑to‑end:
Own problem framing, data exploration, feature engineering, model selection, training/validation, offline/online evaluation, deployment, and ongoing monitoring.

• Focus on the right tools for the job:
Apply binary classification, anomaly detection, and multi‑armed bandits where they provide clear measurable value; avoid over‑engineering.

• Elevate reliability & safety:
Implement robust monitoring (drift, stability, performance, fairness), incident playbooks, and model lifecycle hygiene (versioning, rollback, reproducibility).

• Tell the data story:
Communicate findings and trade‑offs to technical and non‑technical stakeholders; influence priorities with clear narratives and evidence.

• Raise the bar:
Contribute to ML standards, reusable features, and internal communities of practice across EG.

Minimum Qualifications

• Bachelor's, Master's, or PhD in Computer Science, Statistics, Engineering, or a related technical field; or equivalent related professional experience.

• 7+ years (with a Bachelor’s), 5+ years (with a Master’s), or 4+ years (with a PhD) of professional experience in data science or machine learning roles.

• Proficient coding skills in Python or Scala, with experience writing clean, maintainable, and optimized ML code.

• Deep understanding of supervised learning, anomaly detection, and model evaluation techniques.

• Experience deploying ML models in production environments.

• Proven ability to translate ambiguous business problems into actionable ML solutions.

• Proficient communication and stakeholder management skills.

Preferred Qualifications

• Experience in payments or financial systems, with an understanding of the domain's complexity.

• Familiarity with multi‑armed bandits, anomaly detection, and binary classification models.

• Experience…