ML Data Scientist, Payments

As an ML Data Scientist on eBay's Payments Engineering team, you will design and build ML systems to secure and optimize financial transactions for millions of users. This role involves applying state-of-the-art machine learning, from deep learning to generative AI, in a large-scale e-commerce environment. You will develop real-time, intelligent systems, emphasizing novel algorithm design, innovative work, and pushing AI research boundaries to solve real-world problems in payment routing, personalization, and intelligent decisioning.

Must Have

• 4+ years of applied research experience in machine learning or AI
• Proficiency in Python and ML libraries/frameworks (e.g., PyTorch, TensorFlow, JAX)
• Strong mathematical and algorithmic background (optimization, probability, statistics, linear algebra)
• Solid foundation in statistics, predictive modeling, and information retrieval
• Experience with large-scale experimentation, distributed training, and working with big data systems
• Familiarity with real-world applications such as LLM, NLP, computer vision, or recommendation systems
• Knowledge of statistical techniques including regression, time series analysis, hypothesis testing
• Expertise with applying predictive modeling techniques, statistics and information retrieval methods to real-world data
• Hands-on experience with SQL/Hive/Spark, data mining and big data query optimization
• Extensive experience with large data sets and data manipulation
• Excellent problem-solving skills and experience in deploying predictive models in production environments
• MS or Bachelor’s in Computer Science, Machine Learning, Statistics, or related field

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.

Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet.

Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.

At eBay, payments are at the heart of every transaction. As an ML Data Scientist on our Payments Engineering team, you’ll help design and build the ML systems that secure and optimize financial transactions for millions of users worldwide. This is a unique opportunity to apply state-of-the-art machine learning from deep learning to generative AI models in a large-scale, high-traffic e-commerce environment. You’ll work closely with other engineers, data scientists, and product leaders to create real-time, intelligent systems that make buying and selling on eBay safer and more seamless. This role emphasizes designing new algorithms, publishing innovative work, and pushing the boundaries of AI research while applying these breakthroughs to real-world problems.

What you will accomplish:

• Build , Train and deploy ML models for intelligent payment routing, personalization, and intelligent decisioning in payments.
• Perform feature engineering, data preprocessing, and large-scale data analysis.
• Research, design, and implement novel machine learning and AI algorithms across areas such as deep learning, generative models, reinforcement learning, NLP, or computer vision.
• Design, train, and optimize machine learning models for a variety of business applications (classification, regression, recommendation, and personalization).
• Construct robust ML pipelines for training, validation, and deployment using modern ML stacks.
• Apply prompt engineering techniques with Generative AI models (LLMs, diffusion models, etc.) to tackle application-driven problems.
• Leverage vector databases and build/optimize embeddings for search, retrieval, and semantic understanding.
• Conduct large-scale experiments, develop benchmarks, and evaluate new approaches against existing solutions.
• Stay at the forefront of ML research, proactively identifying emerging techniques that can create business and product advantages.
• Knowledge Sharing: Contribute to internal technical discussions, mentoring, and sharing research insights with engineering teams.
• Prototype new approaches and publish in leading ML conferences/journals when applicable.
• Collaborate with engineers and product teams to build robust ML-powered applications.

What You Will Bring:

• 4 + Years of applied research experience in machine learning or AI
• Proficiency in Python and ML libraries/frameworks (e.g., PyTorch, TensorFlow, JAX).
• Strong mathematical and algorithmic background (optimization, probability, statistics, linear algebra).
• Solid foundation in statistics, predictive modeling, and information retrieval.
• Experience with large-scale experimentation, distributed training, and working with big data systems.
• Familiarity with real-world applications such as LLM, NLP, computer vision, or recommendation systems.
• Knowledge of statistical techniques including regression, time series analysis, hypothesis testing, combining disparate data sources.
• Expertise with applying predictive modeling techniques, statistics and information retrieval methods to real-world data
• Hands on experience with SQL/Hive/Spark, data mining and big data query optimization
• Extensive experience with large data sets and data manipulation.
• Excellent problem-solving skills and experience in deploying predictive models in production environments.

Education: MS or Bachelor’s in Computer Science, Machine Learning, Statistics, or related field

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