Research Data Scientist

Preferred Qualifications, Competencies, and Experience

• Professional and/or formal educational experience utilizing large language models for research, including deploying, tuning, and optimizing open- and closed-source models in local and cloud environments, including high-security or air-gapped environments for sensitive data
• Experience applying LLMs to structured information extraction from unstructured text (e.g., named entity recognition, relation extraction, schema-constrained or function-calling-based structured output generation)
• Experience with natural language processing utilizing traditional data science approaches (word2Vec, NLTK , Spark NLP , etc.) and/or NLP utilizing large language models
• High level of proficiency in either R or Python in the areas of machine learning, deep learning, statistical analysis, computer vision, and/or graph analysis
• Experience with data engineering to create data science pipelines, deploy and scale models, and maintain and thoroughly document code and models, including designing evaluation frameworks for model/pipeline accuracy (e.g., precision/recall/F1 against gold-standard human annotation
• Knowledge of SQL programming, data modeling, and relational database systems such as Oracle, MS- SQL , MySQL, etc.
• Highly capable of working in a cross-functional team and functioning as lead scientist on research and development efforts – ability to manage multiple projects at once while delivering high-quality work on time
• Strong interest in clinical data and desire to develop deep expertise in EHR and claims data; interest in developing deep expertise in cancer registry data standards and abstraction workflows (e.g., NAACCR data items, ICD -O-3 histology/topography coding, AJCC staging) is a plus; prior registry experience is not required
• Strong commitment to teaching and disseminating work through writing and speaking engagements, as creating and giving seminars & presentations and authoring manuscripts is an expectation for this position.
• Fluency visualizing data for scientific purposes (e.g., creating figures for manuscripts and presentations)
• Experience and interest in academic research (working with faculty, disseminating work through manuscripts & open-source software, and grant writing)