Principal / Senior Data Scientist, Agentic AI

Arlyn AI•New York, NY

1d•$175,000 - $255,000•Hybrid

About The Position

Arlyn AI is seeking a data-driven problem solver to use large language models (LLMs) to transform unstructured data into data ontologies and actionable insights that support drug launches and patient access strategies. The role works at the intersection of AI, data science, and healthcare, helping biotech and pharmaceutical companies address patient access barriers and optimize commercial decisions. The Principal / Senior Data Scientist will own the end-to-end development and deployment of agentic LLM applications, and design, build, evaluate, and ship production-ready AI systems from prototype through production. The role partners with the founders to shape product direction and works with Engineering on scalable AI infrastructure, system architecture, and internal tools. It also drives rapid experimentation and iteration to improve system performance, reliability, and user experience. The position is based at Hudson Yards in New York City, with the team working on-site three days a week. Arlyn AI supports relocation for candidates not already in the area. The company is unable to sponsor new H-1B visas.

Requirements

Bachelor’s or Master’s degree with 5+ years of industry experience in finance, biotech, or tech startups, or a PhD in a quantitative field such as computer science, computational biology, or statistics with 2+ years of industry experience.
Hands-on experience building AI applications, including prompt engineering, advanced analytics, and transforming large-scale unstructured data into production-ready solutions.
Deep technical expertise and the ability to translate complex, ambiguous business challenges into clear, structured data problems.
Passion for LLMs and real-world AI applications, with a pragmatic, impact-driven mindset.

Responsibilities

Own end-to-end development and deployment of agentic LLM applications that deliver measurable business impact.
Design, build, evaluate, and ship production-ready AI systems from prototype to production.
Partner with founders to define product direction and translate business problems into AI solutions.
Collaborate with Engineering to build scalable AI infrastructure, system architecture, and internal tools.
Improve system performance, reliability, and user experience through rapid experimentation and iteration.
Collaborate across Engineering and Data Science teams to lead decision-making on essential system architectures and core operational workflows.