Research Data Engineer II, CHeT Analytics

Job Summary

As a community, the University of Rochester is defined by a deep commitment to Meliora - Ever Better. Embedded in that ideal are the values we share: equity, leadership, integrity, openness, respect, and accountability. Together, we will set the highest standards for how we treat each other to ensure our community is welcoming to all and is a place where all can thrive.

265 Crittenden Blvd, Rochester, New York, United States of America, 14642

Full time, Regular, Scheduled Weekly

Hours:

40

400980 Neuro-Ctr Health & Tech/Admin

The CHeT Research Data Engineer II will work with faculty, study teams, and industry partners in driving data science for key projects, which include data predictive model analytics for disease progression and observational and interventional studies involving smart apps, wearable sensors, and other new technologies. Under general guidance and supervision, the position has considerable latitude for independent judgment and initiative.

• Data science for Omics and Clinical Disease Progression Analytics.
• Curate, harmonize, and integrate large-scale omics and clinical datasets to support disease progression research.
• Develop and apply computational methods, algorithms, and predictive models to characterize and forecast disease trajectories.
• Build custom data models and multi-omic integration pipelines to uncover biological mechanisms and clinical correlates of disease.
• Design and implement processes, tools, and dashboards to monitor model performance, assess accuracy, and support reproducible research.
• Data science in studies involving smart apps, wearable sensors, and new technologies.
• Use statistics, machine learning, and computer science to analyze and interpret digital data and to compare to traditional assessments and scales.
• Develop custom data models and algorithms to apply to data sets.
• Use predictive modeling to analyze and characterize disease progression.
• Assess the effectiveness and accuracy of new data sources and data gathering techniques.
• Develop processes and tools to monitor and analyze model performance and data accuracy.
• Research & writing & publications related to data science modeling/analytics.
• Disseminate research results through abstracts, publications, and other mechanisms.
• Keep abreast of trends as they relate to the computer field by self-study, attending job-related seminars, courses, or conferences which enhance personal development.

• Bachelor’s degree in Data Science, Computer Science, Mathematics, Statistics, or similar discipline (Required).
• Master’s degree (Preferred).
• 2 years of relevant experience (Required).
• or equivalent combination of education and experience (Required).

The ideal candidate will be a self-starter with a proven ability to solve problems and continually learn new things in a fast‑paced environment.

• Software Analytics, Machine and Deep Learning (e.g., clustering, decision tree learning, artificial neural networks, etc.), Visual analytics, Natural Language Processing, Multimodal Data Fusion, Compressive Sensing, Data architectures.
• Knowledge of advanced statistical techniques and concepts (e.g., regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
• Research & Project Management.
• Coding knowledge and experience in computer languages (e.g., C, C++, Java, R, Python, SQL).
• Excellent communication skills for coordinating across teams.
• A drive to learn and master new technologies and techniques.

The University of Rochester is committed to fostering, cultivating, and preserving an inclusive and welcoming culture to advance the University’s Mission to Learn, Discover, Heal, Create – and Make the World Ever Better. In support of our values and those of our society, the University is committed to not discriminating on the basis of age, color, disability, ethnicity, gender identity or expression, genetic information, marital status, military/veteran status, national origin, race, religion, creed, sex, sexual orientation, citizenship status, or any other characteristic protected by federal, state, or local law (Protected Characteristics).

This commitment extends to non‑discrimination in the administration of our policies, admissions, employment, access, and recruitment of candidates, for all persons consistent with our values and based on applicable law.