Computational Genomic Scientist

The Gastrointestinal (GI) Oncology Translational Lab at Weill Cornell Medicine in New York City is seeking a highly skilled computational biologist with a strong analytical background, driven to tackle the mysteries of biology with high-throughput data. We are looking for a bright and motivated person to join our research program to study tumor microenvironments, unique molecular disease subtypes, and their relation to resistance and sensitivity to cytotoxic therapy and immunotherapies. The Translational Lab (PI Manish A. Shah, MD) in the GI Oncology program at Weill Cornell is a multidisciplinary group focused on novel drug and immunotherapy development across GI malignancies (https://shahlab.weill.cornell.edu/).  Drivers of resistance including disease subtypes, the tumor microenvironment, and host factors are explored in preclinical models and from blood and tissue samples across an array of patients treated on clinical trials.

This position combines the best aspects of industry and academia; it is ideal for PhD graduates looking to work in a thriving and stimulating research environment on cutting-edge medical and clinical research projects, directly impacting multiple research efforts. The focus of our work is to understand the data at hand and how it relates to the initial research question. If you enjoy detail-oriented data exploration, hypothesis testing, writing robust code, and contributing to a wide range of research projects, then this job is for you!

As a computational genomic scientist in the GI translational lab, you will perform in-depth analyses of high-throughput data (single-cell of various modalities, bulk RNA-seq, CITE-seq, ATAC-seq, ChIP-seq/CUT&TAG, and more …) to address a multitude of biological or clinical questions at the forefront of research across GI Oncology. You will have access to resources and mentorship at the Weill Cornell computational biology community. The computational biology program at WCM is staffed by renowned faculty and researchers with extensive expertise in analyzing biological data from diverse types of high-throughput experiments, developing ML algorithms, and building standardized workflows with an emphasis on thorough quality control and statistical rigor.

Central to this position is the ability to quickly develop a deep understanding of the underlying biology that each project addresses and the scope and limitations of the individual experimental methods used. We are looking for candidates with deep knowledge of genomics, cell biology, and experimental protocols related to DNA sequencing and/or other ‘omics data types who are passionate about science and data analysis.

You will:

• Be responsible for designing and carrying out bioinformatics and biostatistical analyses of next-generation sequencing (NGS) datasets from a variety of platforms, including Illumina and 10X Genomics. This includes a strong focus on quality control and critical assessment of the experimental design as well as normalization, clustering, visualization, cellular annotation (for single-cell assays), and additional downstream analyses.
• Critically review, analyze, summarize, and communicate results as well as limitations of analyses to research faculty, staff, and clinical collaborators.
• Prepare results for publications, work with collaborators in writing publications, and optimize visualizations.
• Write robust code that is rigorously documented and liberally commented and can easily be shared via code repositories.
• Organize data in a systematic, resource-conscious way. Curate metadata and upload data sets to public repositories.
• Actively participate in identifying, evaluating, and benchmarking new computational tools and methodologies that will meet the scientific goals of the GI oncology research.
• Educate faculty and lab members in contemporary bioinformatics methods and best practices via small group training and one-on-one consulting.

Qualifications we are looking for:

• PhD in Statistical/Computational Genomics or Bioinformatics, with a strong background in the biological sciences.
• Expertise in Unix environment, shell scripting, high-performance computing, cloud and cluster environments, and version control with git.
• Intimate knowledge of common high-throughput sequencing analysis tools such as short-read/sequence aligners (STAR, salmon, BWA…), single-cell analysis packages (Bioconductor, scanpy, Seurat…), visualization approaches, and functional annotation of sequencing data.
• Strong programming skills in at least one of Python, R, Julia; or compiled languages such as C/C++, Rust, and JAVA.
• Critical and creative thinking, detail-oriented work, enthusiasm to work on team projects, effective communication and presentation skills, never-ending curiosity, problem-solving, and passion for educating fellow staff, students, and faculty about bioinformatics principles.