Staff Applied Scientist, Personalization

Role overview A staff-level applied scientist role within a consumer fintech's AI and machine learning group, owning personalization systems that touch every surface of an all-in-one banking, credit, savings, lending, investing, and crypto product. The position blends research-grade modeling with production deployment, shaping how millions of users discover and interact with financial services. It is well suited to a builder who wants to ship LLM, deep learning, and classical ML systems end to end.

Responsibilities - Design, build, and deploy machine learning, deep learning, and large language model systems that shape customer experience, drive growth, and improve operational efficiency, partnering closely with product, engineering, and analytics. - Develop personalization and recommendation systems that deliver dynamic, user-centric experiences across multiple product surfaces. - Design and optimize search and retrieval systems to improve discoverability, relevance, and user satisfaction. - Lead AI initiatives from ideation through deployment, setting technical direction and sharing best practices across the applied science team. - Explore and productionize LLM-powered applications and agentic systems alongside traditional ranking, recommendation, and personalization models.

Requirements - Seven or more years of experience building and productionizing ML and AI models that deliver measurable business impact. - Hands-on experience with LLM-powered applications, agentic systems, and classical ML techniques including ranking, recommendations, and personalization. - Strong technical background supported by a degree in computer science, data science, applied mathematics, or a related field. - Proven ability to collaborate with product, engineering, and analytics teams to bring AI solutions from concept to deployment. - A builder-and-executor mindset with strong drive and proactivity, comfortable operating in a fast-moving environment.

Nice to have - Experience applying AI to financial services or other regulated consumer products. - Background in large-scale recommendation, search, or retrieval systems.

Benefits and work setup Compensation includes a competitive base salary, stock options, and health benefits starting day one, alongside a 401(k) plan with company match. The role is remote-friendly within the US, with flexible time off and growth opportunities in a high-growth, mission-driven culture. The standard interview process runs through an initial talent-partner conversation, a technical or hiring-manager interview, a team interview, an executive interview, and an offer.