Thank you for your interest in our Research Data Analyst position. Please be advised Stanford's application system is currently undergoing a transition to a new recruiting platform. As part of this process, all active job postings were removed from the Stanford Careers website on June 1, 2026, and the application system is unavailable during the transition period.

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Thank you for your interest in joining our team, and we appreciate your patience during this transition.

 

Stanford University is seeking a Precision Psychiatry and Neuroimaging Data Analyst with a focus in mental health to manage and analyze large amounts of functional neuroimaging, behavioral and symptom data for classification and prediction of outcomes in depression, under the direction of Dr. Leanne Williams in the Personalized and Translational Neuroscience Lab, PanLab. 

 

The PanLab is a major lab with the Stanford Center for Precision Mental Health and Wellness. The lab integrates large-scale functional magnetic resonance data with clinical information and behavioral performance data to classify brain circuit-based subgroups of depression and anxiety and predict personalized treatment outcomes. Our goal is to understand depression as a disorder of functional brain circuits at the individual level, and to drive innovation of personalized treatment approaches that go beyond the current ‘trial-and-error’ approach.

 

Important tasks for the Precision Psychiatry and Neuroimaging Data Analyst span three inter-related categories:

• Organize neuroimaging and relevant behavioral and clinical metadata in data structures. Data will come from the lab’s large number of existing datasets and new data acquired in longitudinal studies.

• Visualize these data, process neuroimaging data and implement machine learning algorithms and models to these neuroimaging and metadata, including predictive models, latent variable, clustering algorithms and canonical correlation analyses.

• Oversee communication and data sharing with an external NIH data coordinating center

 

The ideal candidate will be collaborative, organized, and able to multitask. Because the position relies on real-time interaction it is fully in person. The position contributes to a large NIH project and a multi-year commitment is preferred.  The position will be based within a collaborative team that values cooperation, fairness, efficiency, and conscientiousness. Due to the nature of this position, there is an opportunity for high impact publications, exposure to cutting-edge approaches in the new frontier of precision mental health and opportunities for growth.  

 

Interested candidates should include:

    •  A cover letter addressing why you are motivated to apply, your interest in precision mental health            and how your education and hands on experience relate to the position requirements. Include       

        three references.

    •  Resume or CV

 

To learn more about the lab:

Supervising PI Dr. Leanne Williams

PanLab

Stanford Center for Precision Mental Health 

 

Specific Tasks include:

    •  Collect, manage, and clean large datasets.

    •  Employ new and existing tools to interpret, analyze, and visualize multivariate relationships in                    human subjects’ data.

    •  Create databases and reports, develop algorithms and statistical models, and perform statistical              analyses appropriate to data and reporting requirements.

    •  Use system reports and analyses to identify potentially problematic data, make corrections, and

        determine root cause for data problems from input errors or inadequate field edits, and suggest              possible solutions.

    •  Develop reports, charts, graphs and tables for use by investigators and for publication and                        presentation.

    •  Analyze data processes in documentation.

    •  Collaborate with faculty and research staff on data collection and analysis methods.

    •  Provide documentation based on audit and reporting criteria to investigators and research staff.

    •  Communicate with grant agencies regarding data sharing centers.

 

* - The job duties listed are typical examples of work performed by positions in this job classification and are not designed to contain or be interpreted as a comprehensive inventory of all duties, tasks, and responsibilities. Specific duties and responsibilities may vary depending on department or program needs without changing the general nature and scope of the job or level of responsibility. Employees may also perform other duties as assigned.

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DESIRED QUALIFICATIONS:

• Strong foundation in data structures for management and storage of data

• Expertise in the integration of high dimensional data with meta-data, data quality, and data processing

• Strong knowledge of machine learning algorithms and models, and underlying statistical principles, including predictive models, latent variable modeling (such as drift diffusion modeling), clustering algorithms and canonical correlation analyses

• Hands on familiarity with functional MRI analysis software, such as SPM, FSL and Freesurfer

• Experience with REDCap database architecture, workflow automation, API integration, and scripting to support scalable data capture, quality assurance, operational reporting, and cross-platform research infrastructure

• Proficiency in statistical programming languages such as R and programming languages such as Python

• Familiarity with data visualization and reporting in vector format

• Experience working in research setting relevant to mental health

• Excellent communication and collaboration skills to work effectively with cross-functional teams, including clinical, cognitive, and affective neuroscientists, mental health clinicians, physicians, functional neuroimaging specialists, psychologists and biomedical research engineers

• At least two years of relevant hands-on experience in a quantitative discipline relevant to the domain of the research in the lab, such as psychology, neuroscience, statistics or engineering.

• Motivation to contribute to advances in precision mental health