• Job Type: Officer of Administration
• Bargaining Unit:
• Regular/Temporary: Regular
• End Date if Temporary:
• Hours Per Week: 35
• Standard Work Schedule: Monday - Friday
• Building:
• Salary Range: 165,002 - 200,000

Position Summary

The Department of Biomedical Informatics at Columbia University is seeking a Machine Learning & Medical Imaging Data Engineer with a background in vision-based deep learning to work with Cardiovascular and Radiologic Deep Learning Environment (CRADLE). Some expertise with medical imaging and clinical data science is beneficial but not required. Candidate will work under the supervision of Dr. Pierre Elias.

CRADLE is a pioneering research group focused on developing and implementing AI-driven solutions for cardiovascular and radiologic healthcare. Our mission is to revolutionize patient care through innovative research and cutting-edge technology.  If you are interested in developing and validating cutting-edge machine learning applications and seeing them actually impact clinical care, this is the opportunity for you.

Candidates should be enthusiastic about developing and applying quantitative methods such as computational modelling, image processing or deep learning to problems in cardiology. Ideal candidates have previous experience with machine learning and/or computer vision. A background in computational cardiology and/or computer modeling of the heart is beneficial but not strictly necessary.

Responsibilities

• Analyze and integrate large diverse cardiovascular and imaging datasets to develop predictive models for range of cardiac diseases.
• Design and prototype novel analysis tools and algorithms for detecting undiagnosed disease and predicting patient outcomes
• Collaborate with product, science, engineering, and business development teams to build and bring to market, the most advanced data platform in precision medicine
• Interrogate analytical results for robustness, validity, and out of sample stability
• Document, summarize, and present your findings to a group of peers and stakeholders
• Provide technical leadership & expertise across multiple modeling projects

Minimum Qualifications

Requires a bachelor's degree or equivalent in education and experience; plus, five years of related experience.

Preferred Qualifications

Master’s or PhD degree in a quantitative discipline (e.g. computer science, electrical/computer engineering, machine learning, bioinformatics, statistics, computational biology, applied mathematics, physics, or similar).

Other Requirements

• Strong experience working with cardiac clinical and imaging data and applying AI to solve problems in cardiology.
• Expert-level experience with supervised and unsupervised machine learning algorithms for variety of tasks including classification, segmentation, and transformation.
• Expert-level experience with ensemble methods, such as: PCA, regression, deep neural networks, decision trees, gradient boosting, generalized linear models, mixed effect models, non-linear low dimensional embeddings and clustering.
• Proficient in Python and SQL.
• Experience with the following: Pandas, NumPy, SciPy, Scikit-learn, Jupyter Notebooks, and a machine learning framework such as PyTorch or TensorFlow.
• Strong programming skills.
• Experience with communicating insights and presenting concepts to diverse audiences.
• Team player mindset and ability to work in an interdisciplinary team.
• Goal orientated, self-motivated, and drive to make a positive impact in healthcare.

Equal Opportunity Employer / Disability / Veteran

Columbia University is committed to the hiring of qualified local residents.