Job Description

Overview:

Monte Rosa Therapeutics is seeking an Intern in Bioinformatics Data Sciences to contribute to the elimination of disease-causing proteins through targeted protein degradation--a groundbreaking approach designed to address proteins previously considered undruggable. Successful candidates will gain direct experience within an early-stage drug discovery environment and have the opportunity to focus on areas that align with their interests. You will work closely with industry leaders who will mentor you throughout your project, expose you to additional drug discovery initiatives, and guide you in solving complex biological challenges. Join a diverse, collaborative, and dynamic team that values innovation, teamwork, and a people-centric culture!



If you are interested in contributing to projects in the following areas, you will gain valuable hands-on experience and educational exposure in a fast-paced drug discovery environment


Responsibilities:
#

Bioinformatics & Data Analysis

Design, implement, and optimize end-to-end pipelines for: + Bulk RNA-seq, single-cell RNA-seq.
+ Microarray and legacy transcriptomic platforms.
Perform high-quality differential expression analysis, clustering, pathway enrichment, gene set annotation, and downstream biological interpretation. Curate and integrate data from public repositories (GEO, Single Cell Portal, ArrayExpress, etc.). Apply and benchmark multiple analytical frameworks (Seurat, Scanpy, DESeq2, edgeR, limma, etc).
#

Production & Engineering Collaboration

Work closely with software engineers /IT to productionize bioinformatics pipelines, exposing core functionalities through APIs, dashboards, or internal platforms. Optimize workflows for scalability, reproducibility, and automation (Docker/Snakemake/Nextflow/Prefect/Airflow). Define data standards, schema, and metadata conventions for large-scale bioinformatics data ingestion.

Qualifications:

Currently enrolled in at least a Master's program in Data Science, Bioinformatics, Computational Biology, or a related quantitative field, with a strong interest in life sciences.
Genuine enthusiasm for working at the intersection of software engineering, data engineering, and computational biology, with a passion for building innovative tools and analytical workflows that accelerate drug discovery.
Solid programming in Python and R, with ability to write clean, production-ready code.
Strong experience with RNA-seq pipelines (STAR, Salmon, CellRanger, kallisto, HTSeq, etc.) and downstream processing.
Hands-on experience with single-cell analysis frameworks (Scanpy, Seurat) and data visualization libraries.
Familiarity with microarray processing (affy, limma, oligo) and other transcriptomic modalities.
Strong command of differential expression, normalization strategies, batch correction, variance stabilization, and QC metrics.
Solid understanding of biological pathway databases (MSigDB, KEGG, Reactome, GO) and annotation tools (fgsea, clusterProfiler, Enrichr).
Proven experience with version control, containerization tools (Docker), and reproducible workflow engines.
Previous experience collaborating with software engineers to integrate bioinformatics logic into product interfaces or internal tools.
Understanding of API design, data serialization formats (JSON, Parquet, AnnData, Loom), and modular software architecture.
Ability to translate research-grade notebooks into scalable, maintainable production pipelines.