Requirements:
Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
6+ years applied ML with explicit search and/or recommendation systems experience.
Demonstrated experience designing and building systems at scale — including representation learning, candidate retrieval and ranking with multi-stage pipelines
Proficient in Python and SQL, with experience processing large-scale data in distributed environments (e.g., Spark).
Track record of shipping ML systems that moved business or customer metrics at scale – not just exposure to frameworks or techniques.
Strong foundation in statistics, experimentation, and data analysis, including design of experiments and A/B testing.
Hands-on experience building or rigorously evaluating LLM- and agent-based systems (e.g., RAG, agentic workflows, LLM-based evaluation), with clear judgment on where these techniques apply and where they don’t.
Experience partnering with engineering teams to deploy and maintain machine learning systems in production.
Understanding of real-time systems, model serving, feature pipelines, and monitoring.
Ability to make practical tradeoffs between model complexity, performance, latency, and scalability.
Demonstrated ability to lead technical work across projects and influence direction across data science, engineering, and product teams.
Experience mentoring or guiding other data scientists and contributing to a strong technical culture.
Experience working with cloud platforms such as GCP or Azure.
Experience in retail, e-commerce, or high-scale consumer domains is a plus.