Applied AI Scientist

RA Capital Management•Boston, MA

19h•Hybrid

About The Position

RA Capital's Healthcare Data Strategy team uses data-driven analysis to inform investment decisions and improve core business processes. The team’s work spans portfolio management, risk management, and data science, leveraging complex healthcare and financial datasets to identify important trends, patterns, and decision drivers. The team operates in a fast-paced, collaborative environment and works across RA Capital, including with the Investment Team, RA Ventures, Legal, Finance, Compliance, and TechAtlas. Team members also have opportunities to work directly with senior leadership and portfolio managers. As a Senior Applied AI Scientist, you will design and implement AI-driven systems that transform complex, unstructured information into reliable, decision-relevant insights. Working at the intersection of applied AI, data science, and investment research, you will partner closely with investment professionals to translate ambiguous questions into technical solutions and actionable conclusions. This is a hands-on individual contributor role with responsibility for independently owning projects from initial problem definition through deployment and ongoing improvement.

Requirements

Bachelor’s or advanced degree in computer science, data science, engineering, statistics, applied mathematics, computational biology, or another related quantitative or technical field.
At least 4 years of professional experience in applied machine learning, data science, software engineering, data engineering, or a related discipline, including at least 2 years working with NLP, information-retrieval, or LLM-based systems.
Hands-on experience with one or more of the following: NLP workflows, knowledge graphs, vector databases, embedding-based retrieval, entity extraction and linking, RAG, or agentic AI systems.
Experience taking AI or machine-learning solutions beyond initial prototyping, including testing, deployment, evaluation, or ongoing improvement.
Strong Python and SQL skills, along with familiarity with software-engineering practices such as version control, testing, and documentation.
Strong understanding of AI and machine-learning fundamentals, evaluation methods, and the limitations of LLM-based systems.
Ability to independently structure ambiguous problems and communicate complex findings clearly to senior stakeholders.
Must be authorized to work in the United States without current or future sponsorship or transfer of sponsorship.
Must be based in Massachusetts or willing to relocate and able to work a hybrid schedule from RA Capital’s Boston office.

Nice To Haves

Experience working with healthcare, life sciences, investment management, financial services, or other complex domain-specific data.
Familiarity with scientific literature, clinical-trial information, company filings, market research, or other sources relevant to healthcare investing.
Experience with Spark, PySpark, cloud-based AI platforms, or production data pipelines.
Experience working directly with investment, research, business, or executive stakeholders.

Responsibilities

Design and build systems that transform unstructured healthcare, scientific, and financial information into structured, usable insights.
Develop and deploy AI-driven workflows using approaches such as NLP, retrieval-augmented generation, knowledge graphs, embeddings, entity extraction, and agentic systems.
Own applied AI projects from problem definition and prototyping through deployment, evaluation, and iteration.
Develop benchmarks and evaluation frameworks to assess accuracy, relevance, reliability, and potential failure modes.
Partner with investment professionals and other stakeholders to identify high-value use cases and incorporate findings into investment research and decision-making.
Translate complex healthcare, financial, and analytical information into clear narratives, tools, and visualizations for technical and nontechnical audiences.
Help establish best practices for data quality, testing, documentation, reproducibility, and responsible AI use.