Senior Associate Scientist, Computational Biology

Our mission is to develop therapies that empower the immune system to combat neurodegeneration and our team is focused on developing treatments for some of the most challenging diseases facing our society. By harnessing advancements in genetics, immunology and neuroscience, we are pioneering the development of potential therapies to counteract the devastating progression of neurodegeneration. We are supported in this mission by experienced and accomplished scientists and clinicians, a novel portfolio of programs, leading healthcare investors, and pharma company partners, who share in our commitment to bettering the lives of patients and realizing a world where we make brain disorders history. Key Goals & Accountabilities Advance image-based research! Develop, benchmark, and apply computational methods for high-content imaging, including image segmentation, feature extraction, and quantitative single-cell tracking analysis from a broad variety of samples. Evaluate and integrate deep-learning segmentation approaches to improve accuracy and throughput of our models. Modernize our pipelines with AI! Identify, evaluate, and incorporate new AI and machine-learning tools into imaging and multi-omics workflows, improving automation, reproducibility, and the speed of turning raw data into insight. Turn complex data into biological insights! Analyze imaging and multi-omics datasets and partner with scientists across R&D to address key biological questions and inform experimental design. Make scientific data accessible! Manage and organize data from internal and published studies and develop interactive data-exploration portals for internal use. Build efficient and reproducible workflows! Apply best practices in code and data management, including workflow orchestration, version control, and documentation, while identifying opportunities for continuous improvement. Communicate findings clearly! Present analytical results in verbal, visual, and written formats to cross-functional audiences with varying levels of technical expertise. Required Qualifications M.S. with at least three years of relevant experience or Ph.D. with at least one year of relevant experience in biotechnology, computational biology, data science, bioinformatics, or a related field. Hands-on experience analyzing high-content imaging data, including cell segmentation, feature extraction, and qualification, as well as high-dimensional biological datasets, such as bulk or single-cell RNA sequencing or proteomics. Experience applying deep-learning and AI-based methods to image analysis, particularly cell segmentation (e.g., Cellpose, MEDIAR). Proficiency in R and Python, including experience with relevant tools and packages such as DESeq2, Seurat, Scanpy, dplyr, and tidyverse for statistical modeling and data analysis. Familiarity with shell scripting, version control using Git, and cloud-computing environments such as AWS. A fundamental understanding of biology and the ability to apply computational approaches to biological questions. Excellent written and verbal communication skills, with the ability to explain complex analyses to both technical and nontechnical audiences. Preferred Qualifications Experience with deep-learning frameworks such as TensorFlow or PyTorch and interest in evaluating emerging AI tools for biological data analysis. Experience building or optimizing image-analysis pipelines for high-content or microscopy data. Experience mining and analyzing publicly available biological datasets. Experience developing dashboards or interactive visualization tools using Shiny, Dash, or similar platforms. A fundamental understanding of neuroscience or neurodegenerative disease biology.