About the team + role
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!
This role is based in our Menlo Park, CA and New York, NY offices, with in-person attendance expected at least 3 days per week.
What you’ll do
- 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
What you bring
- 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
Preferred Qualifications
- Experience building and evaluating agent-based systems for production use
- Experience developing recommendation, ranking, or personalization systems at scale
- Experience working on AI products in regulated industries such as financial services.