Computational Scientist I - MDL

The Broad Institute•Cambridge, MA

22h•Onsite

About The Position

The Broad Institute of MIT & Harvard is seeking an outstanding and innovative Computational Scientist to join the Methods Development Lab. The core mission of this role is the development of novel, scalable computational methods and robust analytical pipelines for the interrogation of ultra high-throughput single-cell multiomic data. This includes, but is not limited to, advanced analyses of transcriptomics, proteomics, epigenomics, and large-scale perturbation (e.g., Perturb-seq) datasets. The successful candidate will be responsible for critically and creatively dissecting complex single-cell datasets across multiple technology platforms and modalities. We are seeking a highly motivated, collaborative, and strategically minded individual to drive scientific discovery in close partnership with experimental research scientists, who prioritizes shared success and team-based problem solving. The role requires a candidate who can contribute significantly to methods innovation and open-source software development, execute rigorous data analysis, and clearly communicate cutting-edge results to contribute substantively to high-impact manuscripts.

Requirements

Ph.D. degree in Computational Biology, Computer Science, or a related quantitative field with 2+ years of relevant work experience.
Proven expertise in analyzing single-cell RNA-seq and related sequencing modalities, with a strong emphasis on computational methods development and innovation.
Direct experience in the design and analysis of single-cell perturbation screens (e.g., Perturb-seq), including gRNA assignment and on-target efficiency evaluation.
Experience developing computational strategies for the identification, quantification, and functional interpretation of alternatively spliced RNA isoforms from single-cell long-read sequencing data.
Expert proficiency in Python & R for statistical analysis, algorithm development, and data visualization, including demonstrated experience designing, building, and deploying scalable and reproducible analytical pipelines in a cloud computing environment.
Strong foundational knowledge of statistical and machine learning techniques applied to high-dimensional biological data, including data integration, batch correction, and dimensionality reduction.
Strong ability to work both independently and collaboratively. Proven experience working across research groups, with close collaboration in a bench-to-analysis lab setting.
Ability to critically read and discuss scientific research publications, especially those focused on novel computational methodologies.
Excellent written and verbal communication skills, especially for describing complex methods and algorithms.
Ability to thrive in a fast-paced, rapidly changing environment.
Must be able to work onsite in Cambridge, MA five days per week.

Nice To Haves

Publication history in single-cell RNA-seq computational methodology is strongly preferred.

Responsibilities

Innovate and engineer novel computational methods and algorithms to address critical analytical challenges in ultra high-throughput single-cell multiomic datasets.
Design, build, optimize, and rigorously maintain robust analytical pipelines for processing massive single-cell datasets, ensuring scalability, high performance, and reproducibility across HPC and cloud environments.
Contribute significantly to open-source software development by documenting and packaging all novel methods and software for community use and future projects.
Develop and apply specialized analytical techniques for the quantitative assessment of Perturb-seq and related single-cell perturbation screens, including accurate gRNA assignment and on-target knockdown efficiency evaluation.
Execute in-depth, creative analyses on complex single-cell datasets, leveraging both established best practices and newly developed methods to extract critical biological insights.
Collaborate seamlessly with experimental teams, leveraging computational results to strategically guide and optimize the development of novel experimental protocols.
Present results, methodology, and insights clearly at group meetings, and contribute substantively to the writing and preparation of manuscripts and publications.