Sr Data Scientist

PayPal • New York, NY

3d

About The Position

Working closely with ML Engineers, product partners, and data scientists, you'll explore data, prototype models, and drive measurable improvements in customer experiences. Lead the development and implementation of advanced data science models. Collaborate with stakeholders to understand requirements. Drive best practices in data science. Ensure data quality and integrity in all processes. Mentor and guide junior data scientists. Stay updated with the latest trends in data science. Contribute to the design and development of personalization and recommendation models, leveraging advances in ranking, retrieval, reinforcement learning, and generative/agentic AI. Perform in-depth exploratory data analysis to identify new features, signals, and modeling opportunities that improve personalization outcomes. Develop, prototype, and validate machine learning models with a strong focus on scientific rigor, statistical soundness, and measurable business impact. Partner with product managers and engineering teams to operationalize models, define success metrics, and track experiment results. Build and support robust experimentation frameworks, ensuring reliable evaluation of model performance through A/B tests and causal inference methods. Communicate findings, insights, and technical recommendations to cross-functional stakeholders with clarity and influence. Contribute to a culture of scientific excellence, staying current on developments in recommendation systems, ranking algorithms, and personalization techniques. 3+ years relevant experience and a Bachelor's degree OR Any equivalent combination of education and experience. 4-7 years of experience in applied machine learning or data science, with hands-on experience building models for personalization, recommendations, ranking, or related areas. Advanced degree (Master's required; PhD a plus) in Computer Science, Statistics, Mathematics, or a related quantitative field. Strong grounding in machine learning algorithms, statistical modeling, and experimentation methodologies. Proficiency with Python and core ML libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch). Solid experience analyzing large datasets and writing performant SQL. Strong communication skills with the ability to explain complex concepts to technical and non-technical audiences. Demonstrated ability to drive meaningful model improvements through data-driven analysis and experimentation. Experience with large-scale personalization or recommendation systems in production environments. Knowledge of retrieval systems, learning-to-rank, uplift modeling, reinforcement learning, or contextual bandit approaches. Familiarity with cloud ML environments (e.g., GCP, Vertex AI) and feature store concepts. Experience with model monitoring, experimentation platforms, or observability tools (e.g., Datadog). Publication record in ML, RecSys, IR, or related fields is a plus.

Requirements

• 3+ years relevant experience and a Bachelor's degree OR Any equivalent combination of education and experience.
• 4-7 years of experience in applied machine learning or data science, with hands-on experience building models for personalization, recommendations, ranking, or related areas.
• Advanced degree (Master's required; PhD a plus) in Computer Science, Statistics, Mathematics, or a related quantitative field.
• Strong grounding in machine learning algorithms, statistical modeling, and experimentation methodologies.
• Proficiency with Python and core ML libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch).
• Solid experience analyzing large datasets and writing performant SQL.
• Strong communication skills with the ability to explain complex concepts to technical and non-technical audiences.
• Demonstrated ability to drive meaningful model improvements through data-driven analysis and experimentation.
• Experience with large-scale personalization or recommendation systems in production environments.

Nice To Haves

• Knowledge of retrieval systems, learning-to-rank, uplift modeling, reinforcement learning, or contextual bandit approaches.
• Familiarity with cloud ML environments (e.g., GCP, Vertex AI) and feature store concepts.
• Experience with model monitoring, experimentation platforms, or observability tools (e.g., Datadog).
• Publication record in ML, RecSys, IR, or related fields is a plus.

Responsibilities

• Explore data, prototype models, and drive measurable improvements in customer experiences.
• Lead the development and implementation of advanced data science models.
• Collaborate with stakeholders to understand requirements.
• Drive best practices in data science.
• Ensure data quality and integrity in all processes.
• Mentor and guide junior data scientists.
• Stay updated with the latest trends in data science.
• Contribute to the design and development of personalization and recommendation models, leveraging advances in ranking, retrieval, reinforcement learning, and generative/agentic AI.
• Perform in-depth exploratory data analysis to identify new features, signals, and modeling opportunities that improve personalization outcomes.
• Develop, prototype, and validate machine learning models with a strong focus on scientific rigor, statistical soundness, and measurable business impact.
• Partner with product managers and engineering teams to operationalize models, define success metrics, and track experiment results.
• Build and support robust experimentation frameworks, ensuring reliable evaluation of model performance through A/B tests and causal inference methods.
• Communicate findings, insights, and technical recommendations to cross-functional stakeholders with clarity and influence.
• Contribute to a culture of scientific excellence, staying current on developments in recommendation systems, ranking algorithms, and personalization techniques.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees