Sr. Computational Biologist I

Vets Hired • Full-time • New York, New York, United States • 1d ago

About the job Sr. Computational Biologist I

Job Details

Sr. Computational Biologist

Join a dynamic interdisciplinary team of bioinformatic scientists, molecular biologists, and software engineers passionate about advancing cancer research and computational biology. This role focuses on developing and applying analytical methods for cutting-edge genomic research to improve patient outcomes.

Key Responsibilities

Software Development: Create software to advance single-cell genomics research.
Data Analysis: Analyze sequencing datasets to address innovative and clinically relevant research questions.
Troubleshooting: Evaluate results and resolve issues with new analytical methods.
Research and Application: Explore new developments in computational biology and apply them to large-scale datasets.
Collaboration: Partner with researchers to address questions in cancer genomics and interpret results.
Training: Mentor junior analysts in running pipelines, performing quality control, and routine analyses.

Key Qualifications

Education: PhD in Bioinformatics, Computational Biology, or Computer Science with a focus on molecular biology, or equivalent practical experience.
Technical Skills:
- Proficiency in Linux and programming languages like Python or R.
- Familiarity with high-throughput sequence data is advantageous.
- Experience with version control systems such as Git.
Portfolio: Demonstrate coding skills through a code sample or portfolio.

Core Skills

Motivation: Driven to develop impactful software for advancing scientific discovery.
Biological Expertise: Strong interest in molecular and cell biology.
Attention to Detail: Skilled in identifying and addressing data anomalies effectively.
Collaboration: Adept at working with multidisciplinary teams to provide research-focused solutions.
Decision-Making: Strong ability to make timely and effective decisions.
Resilience: Capability to overcome challenges and adapt to new situations.
Learning Agility: Quick to apply knowledge from past experiences to new opportunities.