In this role at PayPal, youll directly impact the business by leading the development of machine learning models and algorithms aimed at enhancing customer satisfaction, retention, and overall experience. As part of the Customer Success Data Science team within the Global Analytics Data Science (GADS) organization, your work aligns with PayPals commitment to leveraging AI/ML-driven automation and Natural Language Processing models to efficiently resolve customer pain points across various channels. Ultimately, your contributions will empower PayPal to deliver personalized, proactive, and effective customer service solutions, ensuring a seamless experience for our users worldwide.
Your day to day
In your day to day role you will
- Lead the design and development of machine learning models and algorithms to enhance customer satisfaction, retention, and overall experience.
- Utilize statistical and machine learning techniques to analyze customer data, identifying patterns and trends, while collaborating with cross-functional teams to translate insights into actionable strategies.
- Stay abreast of emerging ML technologies and methodologies, conducting research and experimentation to drive innovation within the team.
- Mentor and coach junior team members, providing guidance on ML best practices, conducting code reviews, and fostering a collaborative and supportive team environment.
What do you need to bring-
- Bachelors/Masters or Ph.D. in Computer Science, Statistics, Mathematics, or a related field
- 4+ years of experience in Machine/Deep learning and Natural language processing
- Proficiency in programming languages like Python, as well as in ML Frameworks such as PyTorch
- Strong understanding of machine learning algorithms and techniques, with hands-on experience in model development and evaluation
- Experience with Large Language Models (LLMs) such as GPT, BERT, T5, and other transformer-based architectures is preferred
- Proven track record of delivering actionable insights and driving business impact through data-driven decision-making
