Research Associate - Machine Learning

Summary

The Research Associate will be responsible for developing machine learning algorithms and creating predictive models. The ideal candidate must demonstrate a robust background in data science and machine learning, encompassing the application of statistical methods, the creation of predictive models, and the development of machine learning algorithms. Proficiency in Python, MATLAB, and R, combined with experience in signal and image analysis, as well as familiarity with electronic health data, will be distinct advantages. Will support the Director of Data Science in reaching departmental goals and objectives, including the implementation of the ATLAS initiative.

Job Duties

• Plan, direct, and conduct research projects related to surgery and data science in collaboration with faculty. (20%)
• Develop analytical methods and digital capabilities for data consumption, organization, validation, storage, and classification of electronic health data. (20%)
• Prototype, troubleshoot, and fine-tune machine learning models for outcome predictions relevant to surgery. (15%)
• Evaluate machine learning training using tools such as precision-recall metrics, receiver-operator curves, and confusion matrices. (20%)
• Contribute to evidence-based processes and procedures for program quality review and assessment of outcomes data. (10%)
• Present data to faculty leadership. (5%)
• Author software documentation and scientific manuscripts. 5%
• Perform other job related duties as assigned.

Minimum Qualifications

• Master's degree in Basic Science or a related field. Experience in lieu of degree will not be accepted.
• Three years of relevant experience typically as a Research Assistant, Research Technician or Postdoctoral Fellow/Associate.

Department Specific Criteria

• Master's or PhD in Computer Science or a related field
• Experience with machine learning
• Experience with Python, MATLAB and/or R

Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access Employer.

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Requisition ID: 21255