Data Scientist (Knowledge Graph & Identity)

What you'll do

• Own end-to-end delivery of significant data science projects from problem scoping to production deployment focusing on knowledge graph and identity solutions
• Make independently-reasoned decisions on methodology, model selection, and evaluation; document technical solutions clearly
• Lead solution design; break down complex epics into well-scoped user stories with acceptance criteria; adopt DataOps and MLOps best practices
• Build production-quality Python and PySpark code on Databricks; implement advanced ML and AI workflows including entity resolution, probabilistic record linkage, embedding-based matching, semantic similarity, and LLM-augmented pipelines
• Develop and maintain reusable tools, libraries, and documentation to improve team efficiency and standards; conduct code reviews
• Mentor junior data scientists on technical execution, code quality, and career development; lead internal talks or workshops
• Collaborate cross-functionally with product, engineering, and operations; translate business requirements into technical specifications; partner with data engineering on scalable pipeline design; participate in design reviews and working groups

Requirements

• Bachelor's degree in Statistics, Data Science, Computer Science, Mathematics or related quantitative field; Master's preferred
• 3–5 years of hands-on data science experience with ability to deliver complex projects independently
• Advanced Python with production-quality code, testing, and documentation
• Strong SQL and PySpark skills for billion-row datasets
• Experience with Databricks workflows, Delta Lake, and job orchestration
• Working knowledge of cloud platforms (AWS or GCP)
• Solid command of core ML techniques: regression, classification, clustering, model evaluation, experimental design
• Proficiency with MLOps practices: experiment tracking, pipeline orchestration (Airflow), reproducible model deployment
• Exposure to modern AI methodologies: RAG systems, LLM-augmented models, vector databases, semantic search
• Strong communication skills for documentation and cross-functional collaboration
• Ability to mentor junior data scientists and contribute to team standards

Tech stack