Staff Associate II Full-time

The Sims Lab at Columbia University Irving Medical Center is seeking a staff scientist (Staff Associate II) to provide computational biology and bioinformatics support to large-scale immunology and translational cancer biology projects involving perturbation of live human tissues, single-cell genomics, and spatial profiling technologies.  These efforts will involve the implementation of machine learning and statistical modeling tools for analyzing chromatin accessibility, intercellular interactions, and multiplexed lineage tracing data.

Function Knowledge

• (20%) Must have functional knowledge of and experience working with a Linux-based operating system and fluency in at least one high-level programming language (e.g. Python, R, or similar).

Problem Solving

• (20%) Works with a team to troubleshoot computational analysis of genomic data.

Decision Making/Autonomy

• (15%) Works with a team to decide on and benchmark the appropriate software tools for specific experimental data types.

Technical Expertise

• (30%) Must have technical expertise in bioinformatics of genomic and/or transcriptomic sequencing data – particularly single-cell genomics and spatial profiling data.

Communication

• (15%) Must have excellent written communication skills, particularly the ability to clearly record data in written form or on a computer and to write and maintain experimental protocols.

• Bachelor’s degree in biology, chemistry, physics, math, engineering, bioinformatics, computer science or a related field.
• 4 years of experience recommended

Columbia University is an Equal Opportunity Employer / Disability / Veteran

Pay Transparency Disclosure

The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training.  The above hiring range represents the University’s good faith and reasonable estimate of the range of possible compensation at the time of posting.