KER Unit - Health Services Analyst - Supplemental

Mayo Clinic•Rochester, MN

22d•$35 - $53

About The Position

We are seeking a highly motivated researcher with experience in machine learning with Python to develop multimodal AI/ML models from healthcare conversations combined with other sources of data (e.g., scans and tabular data). The ideal candidate will have hands-on experience with textual (NLP), acoustic (speech), and visual data and the ability to integrate these modalities into state-of-the-art models for healthcare applications. The analyst assists with multidisciplinary research analysis using a variety of analytical, quantitative, qualitative, and programming skills. The analyst conducts comprehensive literature searches related to health services research initiatives and compiles and summarizes written and electronic data for research projects. Research funding sources and collects necessary documents for participating in external research collaborations. Writes draft reports that clearly and concisely summarize and illustrate projects findings, outcomes, and results. Drafts sections of grants, abstracts, and manuscripts. Collects and assists in designing data analysis strategies, evaluating data needs and methods of data collection. Implements the data collection protocol. Assists in reviewing study methods, statistical models, and sampling schemes. Trends data to identify potential areas of the investigation supported by graphical presentations. Evaluates the reliability of data sources to ensure data integrity. Coordinates multiple project assignments and is responsible for completing tasks in given timeline. Designs and maintains quantitative and qualitative databases including the management and updating of data.

Requirements

Experience in machine learning with Python
Hands-on experience with textual (NLP), acoustic (speech), and visual data
Ability to integrate multiple data modalities into models for healthcare applications
Analytical, quantitative, qualitative, and programming skills
Experience with PyTorch, Transformers, OpenCV, torchvision, and torchaudio
Experience with prompting strategies, parallel computing, and GPU acceleration
Experience collecting, preprocessing, and managing complex quantitative and qualitative datasets
Experience designing and executing advanced data analysis strategies, including statistical modeling, sampling schemes, and evaluation of computational methods
Experience conducting literature reviews
Experience preparing reports, visualizations, and presentations
Experience drafting sections of grants, abstracts, and manuscripts
Experience identifying external funding opportunities and contributing to research collaborations
Experience managing multiple concurrent assignments, tracking milestones, and ensuring timely project delivery
Experience maintaining and updating quantitative and qualitative databases

Responsibilities

Build and deploy multimodal AI/ML models integrating textual, acoustic, and visual inputs from healthcare conversations and other sources of data (e.g., scans and tabular data)
Implement, train, and fine-tune models in Python using packages such as PyTorch, Transformers, OpenCV, torchvision, and torchaudio.
Apply prompting strategies, parallel computing, and GPU acceleration to optimize large-scale model training and inference.
Collect, preprocess, and manage complex quantitative and qualitative datasets while ensuring data integrity and reproducibility.
Design and execute advanced data analysis strategies, including statistical modeling, sampling schemes, and evaluation of computational methods.
Conduct literature reviews on AI in healthcare and health services research.
Prepare clear and concise reports, visualizations, and presentations of project outcomes.
Draft sections of grants, abstracts, and manuscripts related to AI/ML in healthcare.
Identify external funding opportunities and contribute to multi-institutional research collaborations.
Manage multiple concurrent assignments, track milestones, and ensure timely project delivery.
Maintain and update internal quantitative and qualitative databases