Research Data Engineer II, CHeT Analytics

Location: City of Rochester

Research Data Engineer II, CHeT Analytics page is loaded## Research Data Engineer II, CHeT Analytics locations:
Saunders Research Building time type:
Full time posted on:
Posted Todayjob requisition :
R267068

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.
** Job Location (Full Address):
** 265 Crittenden Blvd, Rochester, New York, United States of America, 14642
** Opening:
** Worker Subtype:

Regular Time Type:

Full time Scheduled Weekly

Hours:

40

Department:400980 Neuro-Ctr Health & Tech/Admin

Work Shift:

UR - Day (United States of America)
Range:

UR URG 113

Compensation Range:$77,216.00 - $
* The referenced pay range represents the minimum and maximum compensation for this job. Individual annual salaries/hourly rates will be set within the job's compensation range, and will be determined by considering factors including, but not limited to, market data, education, experience, qualifications, expertise of the individual, and internal equity considerations.
*** Responsibilities:
** GENERAL PURPOSE  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.
** ESSENTIAL FUNCTIONS
*** 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

Other duties as assigned.

** MINIMUM EDUCATION & EXPERIENCE
*** 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
** KNOWLEDGE,

SKILLS AND ABILITIES

** 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.

Preferred
* 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  &…