Staff Applied Scientist (Personalization)
ONE
full-remoteleadpermanentdataproduct-management Full remote 16 days ago via WTTJ
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Machine LearningDeep LearningLLMsLLM-powered ApplicationsAgentic SystemsPersonalizationRecommendation SystemsRankingData ScienceCross-functional Collaboration
About the role
Role Overview
Join OnePay as a Staff Applied Scientist (Personalization). You will lead AI/ML innovation by designing and deploying machine learning, deep learning, and LLM models that improve customer experience, drive business growth, and enhance operational efficiency.
Key Missions
- Design and deploy ML, deep learning, and LLM models to improve customer experience and business performance.
- Build intelligent AI agents that can reason, plan, and operate across workflows to improve automation and decision-making.
- Develop personalization and recommendation systems delivering dynamic, user-centric experiences across product offerings.
Responsibilities
- Collaborate closely with Product, Engineering, and Analytics teams to take AI solutions from ideation to deployment.
- Partner on the creation of agentic systems and production ML capabilities.
- Drive proactivity and execution as part of a builder-focused culture.
Requirements
- Experience building and productionizing LLM-powered applications, agentic systems, and traditional ML for ranking, recommendations, and personalization.
- 7+ years of experience building and productionizing ML/AI models with measurable business impact.
- Strong technical background with a degree in Computer Science, Data Science, Applied Mathematics, or a related field.
- Ability to collaborate fluently with cross-functional teams (Product/Engineering/Analytics).
Nice-to-Haves
- Not explicitly stated.
About ONE
ONE (OnePay) is a remote-first financial/technology company focused on improving customer experiences through data and AI-driven products. The role centers on applying machine learning, deep learning, and LLMs to personalization, recommendations, and automation to drive measurable business outcomes.
Scraped 6/11/2026