Staff Associate I

The Staff Associate will assist with the application of computational methodologies to identify and pharmacologically target Master Regulators of molecularly distinct, yet co-existing cellular states in a complex set of human malignancies.He/she will interact and work with a team of computational and experimental scientists, providing assistance to the alignment, analysis and interpretation of data generated, including poster and oral presentations.

Functional Knowledge

• Use methodologies and algorithms developed by the lab to analyze scRNA-Seq sequencing data
• Assist with application of computational tools to identify Master Regulators that may
• Be targeted by existing FDA-approved drugs or late stage experimental compounds, using scRNA-Seq data generated in the lab
• Help students and postdocs to generate figures for grant submission and manuscript preparation
• Use of lab-specific computational tools and pipelines for scRNA-Seq analysis
• Data analysis and applied machine learning tools using programming languages such as Python, R, Java, C , JavaScript, MATLAB, SQL
• Basic knowledge of Bioinformatics and Cancer Biology

Problem Solving

• Advanced computational problem-solving abilities, proactive, task-oriented
• Ability to understand research questions and apply proposed solutions, including application of quantitative frameworks to generate quantitative solutions

Decision Making/Autonomy

• Level of supervision required, on a scale of 1 (least) to 10 (most) - # 3
• Degree of independent judgment expected, on a scale of 1 (least) to 10 (most) - # 7

Technical Expertise

• Computer languages and environments: Python, R, Java, C , JavaScript, MATLAB, SQL
• Knowledge of commonly used computational pipelines developed for scRNA-Seq data analysis

Communication

• Excellent communication of science-related topics, including Computer & Data Science, Biomedicine and Engineering

Minimum Qualifications

• Bachelor's degree in Computer & Data Science or related field
• 0-2 years of experience in data science, computational biology and/or bioinformatics