Etsy

Data Scientist

• Join Etsy’s Search Ads team as a Senior Machine Learning Engineer
• Build machine learning systems for sponsored search, including retrieval, ranking, ad allocation, and user engagement modeling
• Develop custom machine-learning models that drive product vision and customer impact
• Maintain scalable pipelines for Etsy’s 90+ million buyers and sellers
• Personalize the ad experience to a buyer’s journey
• Work cross-functionally with product engineering teams and communicate model capabilities to product managers
• Apply advances in machine learning to improve relevance and buyer ad engagement
• Prototype, optimize, and productionize large-scale ML models for the ads funnel
• Conduct A/B experiments to validate ML models and pipelines
• Collaborate with product managers, ML engineers, and analytics partners to measure and improve the ads product

Customer Support

• Design, stand up, and operate Etsy Customer Support’s outsourced enablement delivery model
• Set expectations for partner-led training delivery, trainer capacity, facilitation quality, nesting, refresher training, and ongoing agent development
• Define quality gates, Train-the-Trainer requirements, trainer accreditation, agent certification, and launch-readiness criteria
• Own enablement intake for product launches, policy updates, workflow changes, and operational priorities
• Establish operating rhythms, performance expectations, and escalation paths for partner accountability
• Ensure partner-developed materials are accurate, modular, scenario-based, and practical while maintaining Etsy policy, brand, and quality approvals
• Use QA, CSAT, repeat contacts, nesting performance, early-tenure outcomes, and time-to-proficiency to identify gaps and improve training
• Coordinate enablement plans for launches, workflow changes, seasonal peaks, and high-impact Customer Support initiatives
• Partner with Support Operations, Product, Policy, Quality Insights, TechOps, and subject-matter experts on executable enablement plans
• Establish feedback loops to improve content, delivery methods, agent readiness, and customer outcomes
• Report to the Senior Director, Outsourcing

Customer Support Partner Enablement Manager

Operations

• Set strategic direction for critical fraud verticals and define measurable goals
• Lead and develop a high-performing team of investigators and senior investigators
• Hire, coach, set goals, provide feedback, and develop future leaders
• Own outsourced operations and partner relationships to achieve service-level agreements, quality standards, and operational metrics
• Build investigative capabilities to identify, mitigate, and prevent fraud at scale
• Partner with Product, Engineering, and Data Science on detection, tooling, and ML roadmaps
• Manage capacity, costs, headcount planning, vendors, outsourced partners, and automation investments
• Advise on emerging-risk responses and coordinate high-stakes investigations
• Present data-backed recommendations to executive stakeholders
• Build operating systems including metrics, reviews, runbooks, quality standards, and critical issue paths
• Represent Risk & Fraud in cross-functional and executive forums

Senior Operations Manager, Fraud

Machine Learning Engineer

• Build machine learning systems powering sponsored search, including retrieval, ranking, ad allocation, and user engagement modeling on Etsy.com and the Etsy app
• Work cross-functionally with product engineering teams
• Communicate ideas and model capabilities to product managers
• Apply advances in machine learning to improve relevance and buyer ad engagement
• Prototype, optimize, and productionize large-scale machine learning models
• Conduct A/B experiments to validate ML models and pipelines
• Collaborate with product managers, ML engineers, and analytics partners to measure and improve the ads product
• Report to the Engineering Manager

Senior Machine Learning Engineer, Search Ads

• Write high-quality, production-grade Python code
• Participate in code reviews and pair programming
• Contribute to and help drive architectural decisions
• Build, operate, and improve infrastructure for training and serving machine learning models on Google Cloud Platform and Kubernetes
• Design and maintain serving infrastructure for open-source and internally hosted LLMs
• Manage model deployment and resources, and tune performance
• Apply machine learning fundamentals to infrastructure and design decisions
• Partner with Applied Scientists to understand model training and serving needs
• Translate model requirements into infrastructure that reduces workflow friction
• Contribute to system design discussions, balancing performance, cost, and reliability
• Support infrastructure serving more than 100 million users
• Use generative AI and productivity tools thoughtfully

Data Scientist

Customer Support Partner Enablement Manager

Senior Operations Manager, Fraud

Senior Machine Learning Engineer, Search Ads

Senior Software Engineer, Machine Learning