Expedia Group
Senior Data Scientist (Machine Learning Scientist) – Applied Payments
Expedia Group, Seattle, Washington, us, 98127
This range is provided by Expedia Group. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range $173,000.00/yr - $277,000.00/yr
Additional compensation types Sign‑on bonus and RSUs
Senior Machine Learning Scientist – Applied Payments (Seattle HQ) 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.
In this role, you will:
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 leading ML initiatives with a product‑focused mindset and cross‑functional collaboration.
Exposure to cloud‑based ML infrastructure and data pipelines (e.g., AWS, GCP, Azure).
Contributions to technical communities (e.g., publications, open‑source projects, tech talks).
Strong business acumen and ability to connect ML outcomes to strategic goals.
Expedia Group is proud to offer a wide range of benefits to support employees and their families, including medical/dental/vision, paid time off, and an Employee Assistance Program. To fuel each employee’s passion for travel, we offer a wellness & travel reimbursement, travel discounts, and an International Airlines Travel Agent (IATAN) membership. View our full list of benefits.
Seniority level Mid‑Senior level
Employment type Full‑time
Job function Analyst and Information Technology
Industries Software Development, Data Infrastructure and Analytics, and Technology, Information and Media
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Base pay range $173,000.00/yr - $277,000.00/yr
Additional compensation types Sign‑on bonus and RSUs
Senior Machine Learning Scientist – Applied Payments (Seattle HQ) 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.
In this role, you will:
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 leading ML initiatives with a product‑focused mindset and cross‑functional collaboration.
Exposure to cloud‑based ML infrastructure and data pipelines (e.g., AWS, GCP, Azure).
Contributions to technical communities (e.g., publications, open‑source projects, tech talks).
Strong business acumen and ability to connect ML outcomes to strategic goals.
Expedia Group is proud to offer a wide range of benefits to support employees and their families, including medical/dental/vision, paid time off, and an Employee Assistance Program. To fuel each employee’s passion for travel, we offer a wellness & travel reimbursement, travel discounts, and an International Airlines Travel Agent (IATAN) membership. View our full list of benefits.
Seniority level Mid‑Senior level
Employment type Full‑time
Job function Analyst and Information Technology
Industries Software Development, Data Infrastructure and Analytics, and Technology, Information and Media
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