Associate Principal Scientist, Computational Biologist and Image Analysis Expert Full-time NEW

Computational Biologist

Our company is on a quest for cures and is committed to being the world's premier, most research-intensive biopharmaceutical company. Today, we're doubling down on this goal. Our company's Research Laboratories is a true scientific research facility of tomorrow and will take our company's leading discovery capabilities and world-class small molecule and biologics R&D expertise to create breakthrough science that radically changes the way we approach serious diseases.

The Data, AI & Genome Sciences department is looking for a passionate and talented computational biologist with deep expertise in image analysis to join our Translational Genome Analytics research team. In this role, you will apply AI/ML-based analytics to analyze large multi-scale and multi-omics imaging- and functional genomics-based datasets and closely collaborate with a cross-functional team of computational biologists, data scientists, and stakeholders in Discovery Research to drive target and biomarker discovery and drug development efforts. This role will support multiple therapeutic areas with a strong emphasis on Oncology.

The successful candidate will be a scientifically curious team player, strong communicator, and self-motivated learner with previous demonstrated expertise in computational image analysis, experience with multi-omics data integration and functional genomics assays, and an interest in leveraging AI/ML strategies to aid in our interpretation of complex biological data.

This role is based at our company's Research Labs in Cambridge, MA.

In this exciting role, you will:

• Contribute to multiple stages of drug discovery by interrogating high-throughput functional genomics and cellular profiling assays, including high-content imaging-based screens (e.g., optical pooled screens, cell painting, arrayed CRISPR assays), large-scale NGS-based screens (e.g., pooled CRISPR screens, Perturb-seq), and proteomic datasets.
• Translate biological questions into computational solutions by performing quantitative and rigorous statistical analysis on large-scale in vitro imaging datasets and clinical histopathology images for novel biomarker and target discovery.
• Leverage machine-learning approaches to integrate multiparametric image-derived features with high-throughput phenotypic screens.
• Closely collaborate with a broad variety of stakeholders, including molecular biologists, pathologists, bioinformaticians, and software engineers, and act as a proactive bridge and translator in cross-disciplinary communications.
• Employ best reproducible research and FAIR data practices to generate reusable analysis frameworks and reports to support target identification and validation efforts across therapeutic areas.
• Characterize novel targets coming from genetics, translational and disease pathway exploration, explore target engagement, research mechanisms of action, and provide functional validation of novel drug targets.
• Work with large internal and public biological data sets including Next Generation Sequencing (NGS) data (e.g. RNA-Seq, single cell RNA-Seq, spatial transcriptomics, WGS, CRISPR).

Education Minimum Requirement:

• Ph.D. in Bioinformatics, Computational Biology, Computer Science, Biostatistics, Genetics, Immunology, Mathematics, Molecular Biology, Statistics, or related field and 3+ years of experience.
• Master degree in areas mentioned above with 7+ years of experience.
• Bachelor degree in above areas with 11+ years of experience.
• Experience must be post-degree. Can be a mix of relevant academic or industry experience.

Required Experience and Skills:

• In-depth experience with analyzing high-content cellular and/or tissue imaging assays (e.g., Cell Profiler, scikit‑image, OpenCV, etc.).
• Deep experience with computational analysis (e.g., stain normalization, WSI-level classification, segmentation, etc.) of histopathology imaging (e.g., H&E/IHC whole slide images).
• Advanced programming skills with application in imaging (Python) and statistical analysis (R, tidyverse packages).
• Demonstrated ability to distill complex data into interpretable visualizations (e.g., ggplot2, plotly).
• Demonstrated experience with computational analysis and biological interpretation of diverse large-scale experimental datasets from NGS and arrayed screening.
• Skilled at integrating results generated from multiple omics data sources, and biological knowledge bases to customize analytical approaches for discovery research.
• Experience with AWS cloud computing infrastructure (e.g., S3, EBS, EC2, etc) and HPC Linux environments.
• Experience with version control systems, such as Git, and the ability to collaborate on code bases and adapt to and work with existing analytic frameworks.
• Passion for problem solving and continuous learning; applied both to understanding biology and solving technical challenges.
• Self-motivated, proactive, detail-oriented, and independent phenotype supported by references.
• Excellent verbal and written communication skills for cross-disciplinary collaboration.
• Previous experience as full-time computational biologist.

Preferred Experience and Skills:

• Experience with optical pooled screening (OPS).
• Close familiarity with histology/microanatomy and microscopy readouts.
• Experience with digital pathology.
• Experience with using or developing pipelines with workflow orchestration tools such as Nextflow.
• Hands-on experience with applying AI/ML methods for analysis of image-based biological readouts.
• Interest in identifying novel applications of AI/ML strategies for biological target discovery.
• Experience with and understanding algorithms for DNA-seq, RNA-seq, single-cell RNA-seq and/or functional genomics data.
• Strong publication record.

Required Skills:

Amazon Web Services (AWS), Cell Imaging, Computational Biology, High Content Image Analysis, High Content Imaging, High Performance Computing (HPC), Histology, NGS Data Analysis, Omics, Python (Programming Language), RNA Sequencing, Tissue Histology, Whole Slide Imaging

Preferred Skills:

Digital Pathology, Functional Genomics