About the job
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.
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.
Qualifications
Minimum
5+ years relevant experience and a Bachelor’s degree OR Any equivalent combination of education and experience.
Preferred
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