Senior Scientist Research Computing

Rütgers Ag - Piscataway, NJ

Full-time - Mid Level

Piscataway, NJ

About the position

Rutgers, The State University of New Jersey, is seeking a Senior Scientist in Research Computing within the Office of Advanced Research Computing (OARC). This position is integral to the mission of OARC, which is dedicated to fostering an outstanding environment for research computing at Rutgers. The OARC team is diverse and committed to addressing the evolving computational research challenges faced by the university. The successful candidate will play a key role in developing and adapting data science and machine learning tools tailored for research workflows and analytical pipelines, particularly in the fields of molecular and cellular biology and genomics. This includes engineering new toolsets that incorporate AI models and Deep Learning algorithms developed in-house. In addition to technical responsibilities, the Senior Scientist will support the broader Rutgers University research community and engage in outreach across various campuses, including Rutgers Biomedical and Health Sciences, New Brunswick, Newark, and Camden. This outreach aims to identify potential new users of research computing and data resources. The role requires close collaboration with faculty, post-doctoral associates, students, and research staff to customize workflows, provide training, and offer support. As demand for biomedical and clinical informatics services grows, there will be opportunities to expand the analytical and informatics team. The position is full-time and may offer a hybrid work arrangement, subject to approval and the university's policies. The successful candidate will be part of a team that values diversity, inclusivity, and respect for all perspectives, which are essential for fostering innovation and effective decision-making in research support operations.

Responsibilities

• Develop new and adapt existing data science and machine learning tools for research workflows and analytical pipelines.
• Engineer new toolsets that include AI models and Deep Learning algorithms developed in-house.
• Support the Rutgers University research community in their research computing and data-related research.
• Engage in local outreach on Rutgers campuses to identify potential new users of research computing and data resources.
• Work closely with faculty, post-doctoral associates, students, and research staff to customize workflows, training, and support.

Requirements

• Masters degree or equivalent combination of education and experience.
• A minimum of four (4) years' experience supporting computationally intensive and/or data intensive research projects or equivalent work.
• Strong verbal and written communication skills.
• Ability to work in a dynamic and flexible team environment with a positive attitude.
• Ability to work with minimal supervision and demonstrate a history of self-motivation.
• Experience in at least one of the STEM areas (Science, Technology, Engineering, or Mathematics).
• Strong background in scientific programming.
• Proficient in at least one programming language, preferably Python or R, and work in a Unix-like system.
• Familiarity with standards and best practices for open science and FAIR research software.
• Deep knowledge of statistical methods and practical knowledge of machine learning model building and deployment.
• Experience producing compelling visualizations to tell stories with data.
• Knowledge of research processes and language in biological or medical fields.

Nice-to-haves

• A Ph.D. in a related field.
• Understanding of how advanced research computing technologies enable scientific discovery.
• Familiarity with data-intensive computing environments.
• Experience applying advanced software tools and computing technologies in a diverse research setting.
• Experience with national scientific computing initiatives such as the Open Science Grid (OSG), ACCESS, and CloudLab.
• Familiarity with prominent cloud service providers.
• Understanding user requirements and translating them into functional solutions.
• Knowledge and experience with deep learning frameworks like Tensorflow or PyTorch.
• Experience with statistical tools like SAS, Stata, and SPSS.
• Experience with software containerization, particularly with Docker, Singularity, and K8s.
• Knowledge of HPC systems.

Benefits

• Comprehensive benefit program for eligible employees.
• Health insurance options for full-time postdoctoral fellow students.