Expedia Group
Senior Machine Learning Scientist - Applied Payments
Expedia Group, Seattle, Washington, us, 98127
Senior Machine Learning Scientist - Applied Payments
We are looking for a senior machine learning scientist to lead high‑impact ML initiatives in payments at Expedia Group. The role focuses on building production‑ready models that improve authorization rates, reduce false declines, and increase platform reliability at scale. Responsibilities
Lead a small ML group supporting payments, setting the roadmap and best practices. Partner closely with Product and Payments Engineering to co‑define problems, discover hidden ML opportunities, and align on key metrics. Own end‑to‑end model delivery—problem framing, data exploration, feature engineering, model selection, training, validation, offline/online evaluation, deployment, and ongoing monitoring. Apply the right tools to the problem—binary classification, anomaly detection, and multi‑armed bandits where they provide measurable value. Elevate reliability and safety with robust monitoring, incident playbooks, and model lifecycle hygiene. Communicate findings and trade‑offs to technical and non‑technical stakeholders, influencing priorities with clear narratives. Contribute to internal ML standards, reusable features, and broader communities of practice across Expedia Group. Minimum Qualifications
Bachelor’s, Master’s, or PhD in Computer Science, Statistics, Engineering, or a related technical field (or equivalent experience). 7+ years of data science or ML experience (or 5+ years with a Master’s, or 4+ years with a PhD). Proficient coding in Python or Scala with clean, maintainable, 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. Strong communication and stakeholder management skills. Preferred Qualifications
Experience in payments or financial systems with an understanding of domain 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 (publications, open‑source projects, tech talks). Strong business acumen and ability to connect ML outcomes to strategic goals. Benefits
Full benefits package including exciting travel perks, generous time‑off, parental leave, a flexible work model, travel discounts, and career development resources. Equal Employment Opportunity
We are an equal‑opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age or veteran status. This employer participates in E‑Verify. The employer will provide the SSA and, if necessary, the DHS with information from each new employee’s I‑9 to confirm work authorization.
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We are looking for a senior machine learning scientist to lead high‑impact ML initiatives in payments at Expedia Group. The role focuses on building production‑ready models that improve authorization rates, reduce false declines, and increase platform reliability at scale. Responsibilities
Lead a small ML group supporting payments, setting the roadmap and best practices. Partner closely with Product and Payments Engineering to co‑define problems, discover hidden ML opportunities, and align on key metrics. Own end‑to‑end model delivery—problem framing, data exploration, feature engineering, model selection, training, validation, offline/online evaluation, deployment, and ongoing monitoring. Apply the right tools to the problem—binary classification, anomaly detection, and multi‑armed bandits where they provide measurable value. Elevate reliability and safety with robust monitoring, incident playbooks, and model lifecycle hygiene. Communicate findings and trade‑offs to technical and non‑technical stakeholders, influencing priorities with clear narratives. Contribute to internal ML standards, reusable features, and broader communities of practice across Expedia Group. Minimum Qualifications
Bachelor’s, Master’s, or PhD in Computer Science, Statistics, Engineering, or a related technical field (or equivalent experience). 7+ years of data science or ML experience (or 5+ years with a Master’s, or 4+ years with a PhD). Proficient coding in Python or Scala with clean, maintainable, 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. Strong communication and stakeholder management skills. Preferred Qualifications
Experience in payments or financial systems with an understanding of domain 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 (publications, open‑source projects, tech talks). Strong business acumen and ability to connect ML outcomes to strategic goals. Benefits
Full benefits package including exciting travel perks, generous time‑off, parental leave, a flexible work model, travel discounts, and career development resources. Equal Employment Opportunity
We are an equal‑opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age or veteran status. This employer participates in E‑Verify. The employer will provide the SSA and, if necessary, the DHS with information from each new employee’s I‑9 to confirm work authorization.
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