Apply rigorous analytical thinking and modern AI capabilities to design, build, and scale high-impact solutions
Design, develop, and deploy GenAI-powered internal applications, copilots, and workflow accelerators
Build reusable AI components, including retrieval pipelines, structured prompting patterns, orchestration workflows, and evaluation harnesses
Design retrieval strategies that connect LLMs to trusted internal knowledge sources, ensuring grounded and reliable outputs
Develop and maintain statistical and machine learning models to support automation, optimization, forecasting, and classification use cases
Implement evaluation and validation frameworks to measure quality, accuracy, and consistency of AI-driven systems
Partner cross-functionally to identify high-value opportunities for AI enablement across the organization
Create reusable datasets, feature pipelines, and experimentation frameworks to support iterative development
Uphold high standards for quality, reliability, and responsible AI practices
Contribute to peer review processes to ensure technical rigor and maintainability
Document methodologies, assumptions, and implementation details to ensure transparency and reproducibility

Hands-on experience building applications or workflows powered by large language models (LLMs)
Evidence of a builder mindset through shipped AI tools, internal platforms, or automation solutions
Demonstrated experience applying machine learning techniques in production or enterprise environments
5+ years of relevant experience in Data Science, Machine Learning, or AI-focused roles
Strong curiosity for emerging AI technologies and the ability to evaluate and adopt them responsibly
Academic background in a quantitative discipline such as Statistics, Mathematics, Computer Science, Engineering, Economics, or a related field
Experience with vector databases, embedding models, or semantic retrieval systems (preferred)
Experience designing internal AI platforms or shared enablement frameworks (preferred)
Familiarity with API-driven architectures and integrating AI capabilities into enterprise systems (preferred)
Exposure to responsible AI practices, governance frameworks, or model lifecycle management (preferred)

Senior Data Scientist

Key skills