Senior NLP Data Science Researcher

November 8

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John Snow Labs

WebsiteLinkedInAll Job Openings

Big Data • Digital Health • Data Philanthropy • Data Analytics • Health IT

51 - 200 employees

Founded 2015

🤖 Artificial Intelligence

⚕️ Healthcare Insurance

🧬 Biotechnology

Description

• Join our team as a Senior NLP Data Science Researcher, where you'll apply advanced Generative AI, LLM, and NLP methods in novel Healthcare projects. • This customer-facing role demands a blend of excellent technical prowess, research expertise, and communication skills, with a focus on training and optimizing NLP/LLM models, creating robust Python-based inference pipelines, and conducting thorough data analysis and model validation.

Requirements

• PhD Degree: Must possess a PhD in Computer Science, Bioinformatics, Statistics, Medicine, Life Sciences, or a related field. A strong academic publishing track record is preferred. • NLP & LLM Expertise: Demonstrable hands-on development experience in Natural Language Processing and Large Language Models (LLM). • Software Engineering: Proven experience in coding, software design, and data science is essential. Substantial hands-on expertise with the Python ecosystem of AI libraries is required. • Data Analysis Acumen: Skilled in exploratory data analysis, data preparation for ML/AI, and statistical evaluation of ML models. • Communication: Exceptional oral and written communication skills, including in customer facing interactions. • Research and Publication Skills: Proven ability to take the lead in writing scientific publications. Must have publications from LLM & NLP. • US Residency: Must be a resident of the United States.

Benefits

• Career Growth: This is a career-defining opportunity that promises to expand your skill set and experience with various tools and techniques. • Collaborative Environment: Work within a team of clinical and data science experts to make a significant impact. • State of the Art Technology: Contribute to advancing the state of the art of Healthcare AI, including contributions to open-source projects and peer-reviewed papers. • Learning and Culture: Opportunities for professional development through knowledge exchange within a global AI community.