Research Scientist I - The Shalek Lab

Broad Institute•Cambridge, MA

1d•$88,000 - $124,000•Onsite

About The Position

The Shalek Lab (MIT, Broad Institute, and Ragon Institute) and the Ex Vivo Project (Broad Institute, Dana-Farber Cancer Institute, and Microsoft) seek a Research Scientist I with computational and data science expertise to help engineer and target cell states using large-scale single-cell data. Research centers on a platform linking transcriptional cell states to function through pooled transcription factor (TF) ORF overexpression and other genetic perturbation screens read out by scRNA-seq across cancer lineages, with an emphasis on cell-intrinsic programs driving immune tolerance and therapy resistance. The role focuses on computational analysis: developing scalable pipelines for quality control, benchmarking, and integration of large single-cell and multi-omic datasets, and collaborating with machine learning scientists to develop predictive models of cell state and fitness. The scientist will design and execute analyses, maintain reproducible data pipelines, interpret data, coordinate with wet-lab, machine learning, and other collaborators, and communicate findings to the lab and broader scientific community. The position is remote in its day-to-day collaboration context and requires strong attention to detail, communication, and time-management skills for work across a distributed team. It also offers opportunities to gain leadership skills in an interdisciplinary, technology-driven environment and access to the intellectual resources of the Broad, MIT, Harvard, and Dana-Farber communities. The scientist will lead computational analysis and data integration for the cell-state engineering platform, support the study’s Principal Investigators and collaborators, and train and mentor junior lab members and visiting scholars in the analysis framework.

Requirements

Ph.D. in Bioengineering, Computational Biology, Bioinformatics, Medical Engineering, Data Science, or a related quantitative field.
At least 4 years of relevant research experience applying computational approaches to large-scale single-cell and multi-omic data to solve biomedical problems.
Demonstrated experience analyzing pooled genetic perturbation screens, such as ORF/TF overexpression or CRISPR, with scRNA-seq readouts, including quality control, normalization, batch correction, and benchmarking.
Extensive experience integrating and analyzing multi-omics data, such as genetics, bulk RNA-seq, single-cell RNA-seq, spatial omics, and public atlases, for biomedical research.
Proficiency in Python and R, including experience with the scientific Python ecosystem such as scanpy, pandas, and scikit-learn, and sound software engineering practices such as version control and reproducible workflows.
Experience with Linux environments, high-performance and/or cloud computing, and open-source bioinformatics tools for deriving biological insight from large datasets.
Strong time-management and communication skills to balance analyses among multiple collaborators in a remote work environment.
Ability to multitask, work independently under pressure, work in a team, meet deadlines, and prioritize and balance work for multiple people.
Experience mentoring junior researchers.
High productivity demonstrated through high-quality collaborative science and peer-reviewed research publications listing the candidate as a co-author.

Nice To Haves

Familiarity with perturbation-screen design and the experimental workflows that generate the data.
Experience collaborating with machine learning scientists, including preparing training datasets, benchmarking predictive models, or applying machine learning methods to biological data.

Responsibilities

Lead computational design and analysis of pooled TF-ORF and other perturbation screens with scRNA-seq readouts, including benchmarking, quality-control frameworks, and end-to-end analyses to map transcriptional states to fitness and functional phenotypes.
Build, document, and maintain scalable, reproducible workflows for large-scale single-cell data processing on high-performance and cloud computing infrastructure, including multiplexed sample demultiplexing and batch-effect correction.
Integrate perturbation-screen, bulk and single-cell RNA-seq, public cell atlas, spatial, other multi-omic, and genomic datasets to identify gene programs and regulators of cancer cell-state transitions, immune tolerance, and drug resistance.
Collaborate with machine learning scientists to prepare structured, well-annotated datasets for model training, evaluate model performance, and design experiments to validate model predictions.
Communicate study findings and present data at lab, institute, and external meetings; contribute to manuscripts and preprints.
Collaborate with experimental biologists, computational scientists, clinicians, and industry scientists across the group and with external partners.
Train, mentor, and supervise junior computational and experimental researchers, rotation students, and visiting scholars.
Design and execute analyses, maintain reproducible data pipelines, and coordinate with experimental and machine learning collaborators.