Columbia University in the City of New York

Associate Research Scientist (Data Scientist) Team Science, Theory Center

The Zuckerman Institute is seeking a Data Scientist with a strong interest in neuroscience and behavior with affinity for large mixed data sets and machine learning.

Position Summary
Columbia University's Mortimer B. Zuckerman Mind Brain Behavior Institute (the Zuckerman Institute) brings together researchers to explore aspects of mind and brain, through the exchange of ideas and active collaboration. The Zuckerman Institute's home is the Jerome L. Greene Science Center on Columbia's Manhattanville campus. Situated in the heart of Manhattan, at full capacity the Zuckerman Institute will house over 50 laboratories employing a broad range of interdisciplinary approaches to transform our understanding of the mind and brain. In this highly collaborative environment, labs work together to gain critical insights into human health by exploring how the brain develops, performs, endures and recovers from trauma or disease. 

The Zuckerman Institute is seeking a Data Scientist with a strong interest in neuroscience and behavior with affinity for large mixed data sets and machine learning. Within the Institute, many complex multimodal datasets are produced, including electro- and optical-physiology, behavioral measurements, and high-resolution video, across many model systems. This position will support both experimental and theoretical work through the entire lifecycle of data, including acquisition, analysis, modeling and dissemination. We are seeking a Data Scientist to work closely with our contributing labs, to implement and contribute to the data analysis approaches required to analyze and visualize data, in particular, videography data of behaviors synchronously recorded with physiology. The ideal candidate will play an important role in developing and applying algorithms and user interfaces for behavioral tracking and classification and new programmatic capabilities that promote rigor and accelerate discovery in neuroscience. The ideal candidate will also work alongside other scientific staff, postdocs and students and will collaborate in the data analysis and interpretation of experiments.

This position will report to the Director of Team Science with additional mentoring and guidance from faculty in the Center for Theoretical Neuroscience in the Zuckerman Institute. Candidates at the Associate Research Scientist level are strongly encouraged but this position does not require previous postdoctoral experience.

Columbia University is an Equal Opportunity Employer/Disability/Veteran and is committed to the hiring of qualified local residents.

Columbia University welcomes and strongly encourages applicants from underrepresented minorities in STEM (Blacks or African Americans, Hispanics or Latinos, Native Americans or Alaska Natives, Native Hawaiians and other Pacific Islanders), individuals from disadvantaged backgrounds, veterans, individuals with disabilities and women (particularly from the above categories).


Minimum Qualifications

MD, PhD, or doctorate in Neuroscience or related field. 

A record of outstanding research, commensurate with years of professional experience, as evidenced by collaborations, publications, grants, software outputs, and other scholarly measures of impact.

Strong interpersonal, organizational, and communication skills and the willingness to work with multiple researchers, technical staff, postdoctoral researchers, and doctoral students.


Preferred Qualifications

Strong candidates will be interested and focused on interdisciplinary work. 

Experience participating in open source software development; significant contributions to open source projects are highly valued for this role.

Collaboration with Institute research teams to analyze datasets and write manuscripts.

Ability to contribute to grant proposals.

Familiarity with innovative data analysis approaches in machine learning.

Python programming, GPU coding, version control, unit testing and test-driven design.

Ability to perform work with general/minimal instructions.

Capable of exercising independent judgment when working on standard/routine projects.

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