Staff Data Scientist

PayPal•San Jose, CA

1d•Hybrid

About The Position

We are seeking a Staff Data Scientist to lead the design, development, and deployment of advanced AI systems, including Retrieval-Augmented Generation (RAG), semantic knowledge bases, and MCP (Model Context Protocol) server ecosystems. This role requires deep technical expertise combined with architectural leadership, ownership of end-to-end AI systems, and the ability to drive best practices across teams. You will play a critical role in shaping our AI strategy and delivering scalable, production-grade intelligent systems.

Requirements

5+ years relevant experience and a Bachelor’s degree OR Any equivalent combination of education and experience.
Experience leading large-scale AI initiatives or platforms
Familiarity with fine-tuning techniques (LoRA, PEFT) and model customization
Experience with multi-agent systems and orchestration frameworks
Background in search, recommendation systems, or knowledge graphs
Experience in enterprise or regulated environments (e.g., fintech)
AI / Systems Architecture
Deep expertise in: RAG architectures and retrieval systems, Semantic search and knowledge systems, Vector databases (Pinecone, Weaviate, FAISS, Chroma)
Strong experience with LLMs (OpenAI, Anthropic, open-source models)
Proven experience designing and scaling MCP servers or similar AI system infrastructure
Experience with agent frameworks and tool/skill orchestration
LLMOps / Production Infrastructure
Strong experience launching production AI Agents systems, including: Experiment tracking and evaluation (LangSmith, Weights & Biases, MLflow)
Observability and monitoring (latency, token usage, system reliability)
Evaluation frameworks for RAG (faithfulness, hallucination detection, groundedness)
Embeddings & Retrieval Optimization
Deep understanding of embedding models and semantic similarity
Expertise in improving retrieval quality via: Hybrid search (vector + keyword/BM25)
Reranking models (Cohere Rerank, Hugging Face cross-encoders)
Advanced chunking, indexing, and document processing strategies
Proven ability to optimize end-to-end RAG system performance
Prompting & LLM Engineering
Advanced expertise in: Prompt engineering and optimization
Context design and grounding techniques
Systematic evaluation and iteration of LLM outputs
Data Science & Statistical Leadership
Strong foundation in: Probability and statistics
Hypothesis testing and experimentation
Causal inference and experimental design
Experience defining metrics and evaluation frameworks for complex AI systems
Programming & System Design
Strong proficiency in Python
Experience with: ML/AI frameworks (PyTorch, TensorFlow, Hugging Face)
Data processing (Pandas, NumPy, Spark)
Backend/API development (FastAPI or similar)
Solid understanding of distributed systems and scalable architecture

Responsibilities

Lead and manage data science projects, ensuring timely delivery and alignment with business goals.
Develop and maintain data models, algorithms, and reporting systems to support data analysis and decision-making.
Analyze complex datasets to identify trends, patterns, and insights that drive strategic initiatives.
Collaborate with cross-functional teams to understand data needs and provide actionable insights.
Ensure data quality and integrity through regular audits and validation processes.
Mentor and guide junior data scientists, fostering a culture of continuous learning and improvement.
Lead the design and architecture of end-to-end AI systems, including: RAG pipeline, Semantic knowledge bases, Vector search and retrieval systems
Define and implement best practices for LLM-based systems, including prompting, evaluation, and system design
Architect, build, and scale MCP servers and integrate them into broader AI and product ecosystems
Drive development of LLMOps frameworks, including evaluation, monitoring, and continuous improvement of AI systems
Design and optimize retrieval pipelines, including embeddings, indexing, ranking, and hybrid search
Mentor and guide junior data scientists and engineers on AI system design and statistical rigor
Collaborate cross-functionally with engineering, product, and leadership to translate business problems into scalable AI solutions
Establish metrics, experimentation frameworks, and statistical validation approaches for AI system performance
Architect and deliver scalable, production-grade AI systems
Establish best practices for RAG, LLMOps, and retrieval optimization
Drive measurable improvements in system accuracy, latency, and reliability
Influence technical direction and elevate AI capabilities across the organization