Senior Scientist, High Content Screening Analytics, Functional Genomics

Job Description

The Data, AI, and Genome Sciences department is looking for a passionate and talented computational biologist with expertise in the analysis of complex multi-readout and multi-scale genomics assays and imaging screens to join our Translational Genome Analytics research team.

In this role, you will be at the cutting-edge of discovery research, analyzing genome engineering and functional genomic screens at the bulk and single-cell scales and collaborating with cross-functional teams of computational biologists, data scientists, and colleagues in Discovery Research to drive target discovery in our drug development efforts across therapeutic areas, including oncology, neurology, and immunology.

The successful candidate will be a scientifically curious team player and self-motivated learner with previous knowledge of functional genomics-driven experimental strategies for investigating biology and an interest in developing and using technology solutions (algorithms/tools/etc) to aid in our interpretation of complex biological data.

This role is based at Research Labs in Cambridge, MA.

In this exciting role, you will:

DIRECTLY IMPACT OUR EARLY DISCOVERY EFFORTS THROUGH RIGOROUS DESIGN AND ANALYSES OF HIGH CONTENT SCREENS:

• Contribute to multiple stages of drug discovery by interrogating high-throughput functional genomics and cellular profiling assays, including in vitro and in vivo pooled/arrayed CRISPR, single-cell transcriptomics, optical pooled screens, cell painting, and proteomic datasets.

• Perform statistically rigorous quantitative analyses while employing reproducible research and data integrity practices on large-scale datasets from different types of high-content, high-throughput assays.

• Analyze, interpret, and summarize complex biological data and employ data visualization tools to facilitate communication of findings to stakeholders.

• Integrate findings from functional genomics screens and other data sets (including Next Generation Sequencing (NGS) data from, e.g., RNA-Seq, DRUG-seq, single cell RNA-Seq, spatial transcriptomics, WGS, CRISPR) with additional biological data streams to generate biological hypotheses and identify novel drug targets.

REPRESENT COMPUTATIONAL EXPERTISE IN CROSS-FUNCTIONAL TEAMS:

• Provide clear interpretation of high-throughput genetic screening data and propose pathways for further computational and biological validation to our partners and collaborators in Discovery branches across Therapeutic Areas.

• Engage with Discovery Teams to translate biological hypotheses into computational interrogations of functional genomics data as well as multi-omics molecular profiles and actively and iteratively drive experimental design decisions in collaborations.

• Work in a highly collaborative environment, tightly embedded in project teams across scientific disciplines and functions, bringing together genetics, chemistry, pharmacology, and molecular biology to accelerate the discovery of novel drug targets.

EXPAND YOUR KNOWLEDGE AND GROW YOUR CAREER:

• Survey relevant literature on novel bioinformatics analysis methodologies and algorithms and introduce them into in-house workflows where appropriate.

• Keep up to date on other project-relevant literature (experimental and analytics topics in functional genomics space, specific disease biology, etc) and attend relevant conferences.

• Further your professional development by participating in workshops and seminars aimed at personal and professional growth.

Required Education and Experience:

• Ph.D. in Bioinformatics, Computational Biology, Computer Science, Biostatistics, Genetics, Immunology, Mathematics, Molecular Biology, Statistics, or related.

• Masters in areas mentioned above with 4+ years of experience.

• Bachelor's in above areas with 8+ years of experience.

• Experience must be post-degree. Can be a mix of relevant academic or industrial experience.

Required Experience and Skills:

APPLIED EXPERIENCE:

• Demonstrated experience with the computational analysis and interpretation of large-scale functional genomics screens such as pooled/arrayed CRISPR-based screens, PRISM, and perturbational single-cell assays (e.g., Perturb-seq, optical pooled screening), etc.

• Demonstrated experience with computational analysis and biological interpretation of diverse large-scale NGS experimental datasets (e.g., RNAseq, WES, scRNAseq, spatial transcriptomics, cell painting etc.).

• Demonstrated experience with statistical hypothesis testing methodo