About Ramona

At Ramona, we've reimagined microscopy for the modern researcher. Our Multi-Camera Array Microscope (MCAM™) is the first of its kind to offer video-speed capture of cellular detail across an entire well plate. By equipping scientists with unprecedented speed, precision, and insight, we’re on a mission to advance human health and insight through computational microscopy.

About the role

We are seeking a data scientist to advance our cell profiling and computational microscopy platform, transforming large-scale imaging data into biological insight. You will work closely with biologists, microscopists, and software engineers to design and deploy data-driven methods that extract meaningful cellular phenotypes from complex image data. In this role, you will help build scalable data analysis pipelines and backend systems that enable high-throughput cell profiling, reproducible science, and efficient data exploration. We are looking for a data scientist who is excited to operate at the intersection of imaging, machine learning, and biology, helping lay the foundation for scalable cell-level analysis.

Key responsibilities

• Develop and apply data science and machine learning methods for cell profiling, segmentation, tracking, and phenotypic analysis from microscopy images.
• Apply computational methods to integrate genomics, transcriptomics, and proteomics data with high-content microscopy datasets to enable multimodal cellular phenotyping and biological insight discovery.
• Collaborate with a cross-disciplinary team to design scalable data representations and analysis workflows for high-content microscopy datasets.
• Build and optimize data pipelines that support large-scale image analysis, feature extraction, and downstream statistical modeling.
• Contribute to the design and maintenance of backend infrastructure that supports reproducible analysis, storage, and retrieval of cell-level data.
• Document models, analysis workflows, and data schemas for internal teams and external collaborators.
• Evaluate and integrate emerging methods in computational biology, machine learning, and imaging to improve scalability, accuracy, and interpretability.

Qualifications

Required:

• Bachelor’s or Master’s degree in Data Science, Computer Science, Biomedical Engineering, Computational Biology, or a related field (or equivalent experience).
• 2+ years of experience
• Strong proficiency in Python, with experience in scientific computing, data analysis, and machine learning workflows.
• Hands-on experience analyzing microscopy or imaging data, particularly for cell profiling, segmentation, tracking, or feature extraction.
• Familiarity with image analysis and machine learning libraries (e.g., NumPy, SciPy, scikit-image, PyTorch, TensorFlow, or similar).
• Experience working with large scientific datasets and data formats such as HDF5, Zarr, TIFF, or related serialization/storage systems.
• Knowledge of statistical analysis, model evaluation, and data visualization for biological data.
• Experience working with multimodal biological datasets, including genomics, transcriptomics, and proteomics data.
• Strong scientific curiosity, problem-solving skills, and attention to biological and computational detail.
• A commitment to clear communication, collaboration, and scientific integrity.
• Experience with  multimodal biological data integration.

Preferred:

• Master’s or Ph.D degree
• 5+ years of experience