Research Scientist I - The Shalek Lab

Broad Institute•Cambridge, MA

1d•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 motivated Research Scientist I with strong computational and data science expertise. The position focuses on learning, engineering, and targeting cell states using large-scale single-cell data. Research centers on a forward-engineering platform connecting transcriptional cell states to function, including pooled transcription factor (TF) ORF overexpression and other genetic perturbation screens read out by scRNA-seq across cancer lineages, with emphasis on cell-intrinsic programs driving immune tolerance and therapy resistance. The work is primarily computational: building 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 role involves rigorous study design, large-scale data analysis and interpretation, coordination with wet-lab teams and partners, and communication of findings to the lab and broader scientific community. It requires attention to detail, communication and time-management skills, and effective collaboration in a remote, distributed team. The position offers the opportunity to develop team-leadership skills in an interdisciplinary, technology-driven environment and access to the intellectual resources of the Broad, MIT, Harvard, and Dana-Farber communities. The Research Scientist will lead computational analysis and data integration for the cell-state engineering platform and support the study’s Principal Investigators and collaborators. Responsibilities include designing and executing analyses, maintaining reproducible data pipelines, coordinating with experimental and machine learning collaborators while working remotely, and training and mentoring junior lab members and visiting scholars in using and extending 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 computing and/or cloud computing, and open-source bioinformatics tools for deriving biological insights from large datasets.
Strong time-management and communication skills to balance multiple analyses among collaborators in a remote work environment.
Ability to multitask, work independently under pressure, work in a team, meet deadlines, and prioritize and balance work from multiple individuals.
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, such as 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 analysis pipelines 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 produce structured, well-annotated model-training datasets, 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 colleagues and external partners across disciplines, including experimental biologists, computational scientists, clinicians, and industry scientists.
Train, mentor, and supervise junior computational and experimental researchers, rotation students, and visiting scholars.
Support the study’s Principal Investigators and collaborators through computational analysis and data integration.
Design and execute analyses, maintain reproducible data pipelines, and coordinate with experimental and machine learning collaborators while working remotely.