Data Scientist, Robinhood

• You have strong Python and SQL skills, with experience building and evaluating machine learning systems end to end
• You have experience with agent-based AI systems, including reasoning loops, tool use, memory, retrieval-augmented generation, and orchestration
• You have experience designing experiments and applying causal inference methods, including A/B testing and measurement design
• You are comfortable working through ambiguous problems and collaborating with partners across product and engineering

Desirable

• The Platforms Data Science team sits at the intersection of customer experience and trust, building the intelligence that powers how Robinhood supports its customers
• The team develops systems that safeguards customers and the platform while making every interaction smarter: from the in-app AI assistant that helps customers research, trade, and manage their portfolios, to the AI-powered support chatbot that resolves issues autonomously, to the machine learning systems that detect and prevent fraud and abuse in real time
• These systems rely on evaluation frameworks and guardrails that maintain reliability and safety across the platform
• You will work with product engineering, product management, and ML infrastructure teams to deliver production-ready AI systems at scale
• Join a team where your work directly shapes how customers interact with Robinhood!
• As a Data Scientist, Agentic (CX), you will lead machine learning development across the customer experience stack
• This includes models and prompts that power multi-agent orchestration, evaluation pipelines that measure model quality at scale, and personalization systems that determine when and how to engage customers
• You will partner closely with product and engineering to improve reasoning, expand tool usage, and strengthen feedback loops between live systems and offline evaluation
• The role offers ownership from experimentation through deployment, with opportunities to apply advanced AI techniques in a regulated environment!
• Build and deploy machine learning models for customer support systems, including intent classification, escalation detection, clarification, summarization, and multi-agent orchestration
• Design evaluation frameworks using LLM-based review methods, human feedback loops, and automated quality metrics to identify regressions before customer impact
• Develop propensity, segmentation, and personalization models that support proactive outreach and tailored AI experiences
• Translate advances in agent architectures into production systems, partnering with engineering on prompt design, retrieval systems, tool use, memory, and orchestration
• Develop systems that maintain response quality and reliability at scale while working with product, engineering, legal, and compliance partners