Data Scientist - Remote

Role overview

This fully remote role, available within the United States, applies statistics, machine learning, deep learning, and generative AI to questions arising in healthcare delivery, research, and business operations. The data scientist develops analytical solutions, creates and evaluates predictive models and AI agents, and works with engineering teams to move validated approaches into dependable production environments.

Responsibilities

- Query, clean, interpret, and investigate large clinical and operational datasets. - Build machine learning pipelines, statistical analyses, AI tools, and agent-based solutions for practical business, research, and clinical needs. - Design validation studies, pilots, metrics, and experiments to assess the quality and usefulness of AI and machine learning systems. - Work with clinicians, researchers, administrative leaders, data specialists, and engineers to define problems and deliver usable results. - Translate open-ended questions into testable hypotheses, select appropriate analytical methods, and communicate findings to technical and non-technical audiences. - Prepare technical documentation and presentations, manage project tasks, and independently research unfamiliar problems and datasets.

Requirements

- Master’s degree with no minimum experience requirement, or bachelor’s degree with at least three years of relevant experience. - Strong knowledge of probability, statistics, scientific computing, data visualization, and predictive modeling for classification, regression, or clustering. - Proficiency in Python and SQL, with experience using common machine learning libraries and relational databases. - Ability to design, train, test, deploy, and monitor data science or AI solutions across their lifecycle. - Ability to work independently, develop creative solutions, manage time, and collaborate across large cross-functional teams. - Experience extracting data from complex sources, evaluating models rigorously, and explaining results clearly.

Nice to have

- Experience with healthcare environments, electronic health record data, cloud services, version control, or Agile delivery practices. - Experience with deep learning, time series, natural language processing, causal inference, large language models, retrieval-augmented generation, embeddings, fine-tuning, prompt engineering, or agentic workflows. - Familiarity with modern LLM frameworks, AI tools and subagents, and model validation or monitoring practices.