Sr. Research Analytics Scientist

Stanford Graduate School of Business (GSB) seeks a Research Analytics Scientist or Senior Research Analytics Scientist to support academic research at the GSB Research Hub. The Data, Analytics, and Research Computing team (DARC) will engage faculty, students, and staff across disciplines to accelerate research through data curation, technical solutions, and training.

• Consult and collaborate with faculty and researchers to understand their research goals and identify technical obstacles and solutions.
• Attend research groups’ meetings and presentations to assist with identifying promising tools and systems and to discuss their computational challenges and requirements.
• Engage researchers on the use of a broad set of cyberinfrastructure systems, tools, and software.
• Provide support for Stanford research computing clusters and storage services, cloud computing, and national resources.

• Formulate innovative technical strategies and engineer them to completion to achieve unique research objectives.
• Consult to develop frameworks that assist researchers in analyzing and visualizing data to uncover insights and support research findings.
• Help facilitate the design and debugging of research workflows with researchers.
• Research and assist in the development and implementation of innovative technical solutions, including machine learning algorithms, text and image processing, API programming, custom full‑stack applications, automated web scraping, and crowd sourcing pipelines.
• Identify appropriate computational platforms (local and external) and facilitate researchers’ use and mastery of them.
• Continuously adapt to evolving technologies and methodologies to accelerate research development and enable new research frontiers.
• Review product demos and provide initial evaluation of potential platforms.
• Read and understand original academic research papers and their associated computer source code; replicate results from diverse fields as needed.

• Collaborate with research leads and teams to develop tutorials, training sessions, and documentation for faculty and research staff.
• Contribute to research and development efforts to enhance the team’s capabilities in research support, including testing new data collection methods and staying abreast of relevant literature.
• Develop and deliver documentation and training for faculty and research staff.
• Help develop and deliver research community learning opportunities, including workshops, boot‑camps, videos, and other learning collateral.
• Collaborate with others to create training schedules and materials, and lead training sessions focused on the use of Stanford cyberinfrastructure services for researchers.

• Assist colleagues and more junior team members by providing guidance and direction on project activities.
• Connect and coordinate interactions between researchers and technology providers.
• Provide regular communications to systems and software/data professionals.
• Partner with researchers to co‑create and co‑learn relevant computing and data capabilities.

• Assist colleagues and more junior team members by providing guidance and direction on project activities.

• Bachelor’s degree and 5‑7 years of experience in computational methods, tools and systems, or a Master’s degree in computer science, data science, statistics, or a related field and 3‑5 years of relevant experience, or a combination of education and relevant experience.
• High level of proficiency in data science and machine learning methodologies and tools.
• Strong programming skills in Python and experience with scientific coding/scripting, preferably in Python, R, Fortran, C++, and various shells. Experience with research software written in MATLAB, R, Julia, Java script, Stata, SAS, or JavaScript.
• Experience installing and debugging complex scientific applications and associated dependencies; expertise in creating and debugging application containers and scientific workflows in a cluster computing or cloud‑based advanced analysis environment.
• Experience with data processing at scale and understanding of appropriate…