Senior Scientist in ML Methods for Spatial Biology

Within the Predictive Biology and AI team, we are seeking enthusiastic candidates for a Senior Scientist position focused on applying modern computational and machine learning methods to spatial biology and related life sciences data. The successful candidate will have a strong research background, a track record of independent scientific contributions, relevant publications, experience solving technical problems creatively, and the ability to implement, evaluate, and adapt computational methods based on current research.

The successful candidate will be based in Cambridge, MA or Lawrenceville, NJ and will serve as scientific partner for experimental and computational collaborators focused on translational research at those sites while being a member of a geographically distributed team of exceptional machine learning researchers. This role requires strong communication skills, comfort working independently in a distributed environment, and a proactive approach to building productive scientific relationships across disciplines and locations.

The candidate will work as part of a multidisciplinary team focused on bringing advanced ML/AI approaches to impactful biological questions. They will collaborate with computational and experimental scientists with expertise in machine learning, structural biology, chemistry, cell therapy, and gene therapy. We encourage applications from candidates with a background in computational method development and an interest in applying innovative computational approaches to life sciences data.

The Role

• Participate in a growing effort to apply advanced computational techniques to the development of novel therapies for neurologic disease, cancer, and hematologic malignancies.
• Act as a local scientific partner and point of connection for experimental and computational collaborators at the Cambridge or Lawrenceville site, enabling effective cross-functional collaboration with a primarily Europe-based team.
• Analyze spatial and other omics data, including spatial transcriptomics, single-cell RNA-seq, ATAC-seq, and related data types.
• Develop, refine, and evaluate computational methods, ML models, and workflows for large biological datasets.
• Formulate translational and discovery questions as computational problems, interpret model outputs in biologically meaningful ways, and generate hypotheses that can guide experimental biology.
• Creatively propose hypotheses and test them rigorously.
• Author scientific reports and present methods, results, and conclusions to a publishable standard.

Basic Qualifications

• Bachelor's Degree 7+ years of academic / industry experience
• Or Master's Degree 5+ years of academic / industry experience
• Or PhD 2+ years of academic / industry experience

Preferred Qualifications

• Ph.D. in machine learning, bioinformatics, computational biology, or a related technical field.
• Experience applying contemporary computational methods to biological problems.
• Publication record in relevant conferences or journals.
• Experience applying and/or developing ML methods to bioimaging, bioinformatics, single-cell omics, or spatial omics problems.
• Experience with at least one ML framework, such as scikit-learn, PyTorch, or TensorFlow.
• Fluency in written and spoken English. Intense curiosity about the biology of disease and eagerness to contribute to interdisciplinary scientific and computational efforts.
• Demonstrated ability to work independently in a distributed team environment, with strong communication skills and a proactive approach to cross-functional collaboration.

• For senior scientist, two or more years of postdoctoral experience in a relevant field.
• Experience analyzing clinical trial-derived data or images to address translational research questions or applying omics data analysis to public datasets for target discovery and prioritization.
• Prior research experience in pharma, biotech, academic, or hospital environments.
• Experience managing multimodal data, including omics, imaging, or time-series signals.
• Experience using cloud-based computing and software engineering tools or frameworks, such as Docker or Git.