Remote Principal Data Scientist - Rockefeller Neuroscience Institute
West Virginia University Research Corporation

Morgantown, West Virginia

This job has expired.


Description

Remote Principal Data Scientist - Rockefeller Neuroscience Institute 

West Virginia University Research Corporation is seeking applications for a Remote Principal Data Scientist with the Rockefeller Neuroscience Institute. 

Rockefeller Neuroscience Institute is the premier multidisciplinary institute for patient care, research, and teaching in West Virginia and the region. We celebrated the opening of our new Innovation Center on May 15, 2019. The RNI’s flagship facilities are located on the Health Sciences campus in Morgantown. Find out more about our outstanding work and contributions today at:  https://wvumedicine.org/rni/rni-homepage/newsroom/

About the Opportunity

The Rockefeller Neuroscience Institute at West Virginia University is seeking applications for a Principal Data Scientist to work with the Research Operations team on data analysis and modeling focused on population health, clinical neuroscience, and human performance.  This position will help lead and mentor the data science team to carry out research and development projects.

At WVU Research Corporation, we strongly believe in work-life balance and keeping time for things we love outside our work. WVU Research Corporation offers a comprehensive benefits package with a variety of options to suit your needs:
•    13 paid holidays (staff holiday calendar)
•    PTO 
•    403(b) retirement savings with a fully vested 3% employee contribution match, (Employees have the option of contributing an additional 1-3% of their earnings to the plan, which is also matched by the WVURC) 
•    A range of health insurance and various other benefits 
•    Dependent Education Scholarship
•    WVU Perks
•    And More!!

What you’ll do:

  • Work independently as well as help lead a data analytic team to carry out data analysis on large data sets, create visualizations, develop and validate models, and generate reports for research protocols associated with clinical, population health, and human performance. 
  • Duties include data mining to select features in order to build and optimize classifiers using machine-learning techniques to be used for models and analytics reports.
  • Identify opportunities and use cases to solve real world challenges using Advanced Analytics models. 
  • Design, build and maintain a scalable automated infrastructure for extraction, transformation, and aggregation of data from various data sources. 
  • Setup projects to drive the business needs
  • Co-create with business / market / functions or IT platforms on data science projects to meet the business need for insights and analytics using sophisticated data science techniques i.e. automation, crunching big data sets, machine learning or AI
  • Provide technical and people leadership to the data science team


Qualifications

  • Master's degree computer science, engineering, physics, mathematics, data, neuroscience, or related field. Ph.D. preferred.
  • Ten (10) years of experience as a data scientist
  • Development of custom data models and algorithm on large data sets
  • Working on or leading data teams
  • Data model validation and optimizing
  • Development of machine-learning techniques
  • Lead the creation of data visualizations
  • Creating and working with data architectures
  • Experience working with healthcare date
  • Advanced knowledge of statistical techniques and concepts
  • Use of statistical computer languages (i.e. Python, R, SQL, MATLAB, JavaScript)
  • Manage teams to creating and optimizing machine-learning algorithms and statistics.
  • Use of data visualization tools
  • Management of databases
  • Good interpersonal skills and ability to work with diverse populations
  • Ability to create, annotate and maintain detailed records
  • Ability to effectively prepare and present information in one-on-one settings in addition to customers, clients, and other employees of the organization
  • Timely and efficient in testing and deployment of statistical tools


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