Job Search

Position Definition:                                                                                                                                   

We are seeking a highly motivated and skilled Data Scientist to join our team focused on understanding the complicated relationship between biomechanical markers and aortic disease. This role will be instrumental in leveraging big data approaches to analyze vast datasets, identify patterns, and develop predictive models.

Essential Functions:

• Data Acquisition and Processing
  • Collaborate with biomechanics researchers and engineers to collect and integrate large-scale biomechanical datasets.
  • Develop efficient data cleaning and preprocessing pipelines to handle diverse data types and ensure data integrity.
• Feature Engineering and Model Development
  • Extract meaningful measures from biomechanical data, potentially including motion capture, force plate, and physiological measurements.
  • Design and implement advanced machine learning models (e.g., deep learning, statistical modeling) to predict aortic disease risk progression.
• Model Evaluation and Validation
  • Develop rigorous evaluation metrics and perform robust statistical analysis to assess model performance.
  • Validate model predictions against clinical outcomes and validate findings across diverse patient populations.
• Data Visualization and Communication
  • Create clear and concise visualizations to communicate complex data relationships and model insights to stakeholders.
  • Present findings to research teams, clinicians, and collaborators in a clear and impactful manner.
• Collaboration and Research
  • Collaborate effectively with researchers, engineers, and clinicians across disciplines to ensure alignment of data analysis with research goals.
  • Contribute to publications, presentations, and grate proposals related to the project.

Job Requirements:

• Master's or PhD degree in Computer Science, Data Science, Statistics, Biostatistics, Biomechanics, or a related field.
• 3+ years of experience in data analysis, machine learning, or biostatistics.
• Strong foundation in statistical modeling, machine learning techniques (e.g., classification, regression, clustering), and data mining.
• Proficiency in programming languages like Python, R, or similar, with experience in data manipulation libraries like pandas, NumPy, Scikit-learn, and TensorFlow/PyTorch.
• Experience with big data technologies (e.g., Hadoop, Spark, cloud computing platforms) is a plus.
• Excellent understanding of biomechanics principles, particularly related to cardiovascular health, is preferred.
• Strong communication, visualization, and presentation skills with the ability to convey technical information effectively to diverse audiences.

Working Conditions/Environment:

The work environment for this position will include an indoor office and a medical research environment.  It may include some minor annoyances such as noise, temperature variations, etc. The incumbent may sustain posture in standing or seated position and may utilize a computer terminal for prolonged periods of time.

The base wage range for this position is $66,560.00 - $93,060.24 per year. Salary and rank will commensurate with the candidate's qualifications and experience. The wage actually offered to a successful candidate will take into account various relevant and non-discriminatory business factors including, without limitation, the candidate’s geographic location, job-related experience, knowledge, and skills, and education, as well as internal equity considerations. A successful candidate may also be eligible to earn additional compensation including bonuses.

NCIRE is an Equal Opportunity Employer. This position requires access to United States Government research under the Veterans Administration. Per Veterans Administration regulations surrounding access to US research, qualified US Citizens will be selected over other individuals eligible to work in the United States. If you are eligible to work in the United States, you may still be considered for this position but only if no qualified US citizens apply.