Bioinformatics Scientist

Celcuity•Minneapolis, MN

About The Position

Reporting to the Bioinformatics Senior Principal Investigator, the Bioinformatics Scientist will focus on complex experimental and clinical bioinformatics data sets. The role involves executing bioinformatics studies, communicating results, and collaborating with R&D, clinical teams, and bioinformaticians. The scientist will also recommend data capture technologies and experimental models, implement and validate bioinformatics analytical advances, and review/improve existing R/Python workflows. Application of statistical predictive models, including AI, to drug discovery and development pipelines is a key aspect, along with preparing reports and manuscripts.

Requirements

A Ph. D or equivalent experience in biology, chemistry or bioinformatics with strong skills in the analysis of - Omics datasets (bulk/single-cell/spatial transcriptomics, WES, epigenomics, metabolomics and/or proteomics) in preclinical and clinical disease models.
Experience using Machine Learning/Artificial Intelligence tools in the context of -Omics , single cell Atlases, clinical disease modeling and/or drug development pipelines.
Strong skills in bioinformatics, coding proficiency in R/Python, cloud computing environments.
Strong understanding of cell biology especially with respect to basic biochemistry, cell signaling, cell replication, transcription, translation processes.

Nice To Haves

Experience with developing user-facing tools for data visualization and reporting (Shiny, Plotly, Quarto, etc) would be desirable.

Responsibilities

Successfully execute bioinformatics studies in collaboration with other R&D wet-lab scientists, clinical teams and bioinformaticians on multiple defined priority areas.
Effectively communicate results in R&D and corporate meetings, prepare abstracts/manuscripts for publications and conferences.
Communicate with external stakeholders and vendors to recommend data capture technologies and pre-clinical experimental models best aligned with bioinformatics and wet-lab research objectives.
Implement and validate newly identified bioinformatics analytical advances using bioinformatics practices for reproducible and modular analysis (markdown documentation, r/v env locks, git, nextflow, snakemake, dockers, conda).
Review and improve existing in-house analysis R/Python workflows for large-scale Omics data using ML/AI integration to achieve efficient end-to-end analysis, scale-up, and predictive models.
Apply statistical predictive models (including AI) to wet-lab experiments and processes typically involved in drug discovery/development pipelines working with teams of scientists.
Prepare reports, presentations, abstracts, and manuscripts as required, following established standards for documentation, reproducibility of code and data integrity.