Digital Pathology Scientist (Imaging & Tissue Analysis)

ODDITY LABS is ODDITY’s cutting-edge biotechnology R&D center, powering science-backed product innovation through the discovery and development of new molecules, probiotics, peptides, and other biological modalities. Unlike pharmaceuticals, ingredient innovation in the beauty and wellness industry has remained stagnant for decades. We are deploying patented, proprietary technologies and capabilities, including AI-based molecule discovery and advanced phenotypic databases, to understand the biological mechanisms that drive cellular behavior and develop uniquely efficacious products for beauty and wellness applications. Innovations from the ODDITY LABS will power game-changing products through ODDITY’s current brands, as well as future brands.

ODDITY operates with business headquarters in New York City, an R&D center in Tel Aviv, Israel, and a biotechnology lab in Boston. Our culture is fast-paced, innovative, agile, and offers every team member the opportunity to drive a big impact.

Job Summary

We are seeking a highly specialized Imaging Scientist with a deep expertise in ex-vivo tissue analysis to join our R&D team. This role is critical for bridging the gap between biological tissue preparation and scalable data insights. You will be responsible for the end-to-end development of our imaging pipeline: from researching and identifying the most biologically relevant stains for skin histology to developing and implementing the mathematical models that extract quantitative data from these samples.

The ideal candidate is a "biologically-aware" programmer who can write high-quality code in Python, possesses a strong biological understanding of the required phenotypic effects and how to measure them.

Key Responsibilities

1. Stain Research & Histology Strategy

• Have a deep understanding of the connection between a desired phenotypic effect (e.g., reduction of melanin) and the ideal staining technique required to measure and quantify it.
• Conduct literature reviews and experimental research to identify the most relevant staining techniques (e.g., H&E, IHC, Multiplex IF) for specific skin pathology and physiological readouts.
• Collaborate with the wet lab team to optimize histology protocols that ensure maximum signal-to-noise ratio for downstream digital analysis.
• Evaluate how different staining intensities and artifacts impact model performance.

2. Image Analysis & Model Development

• Research, design, and validate computational models to analyze high-resolution images of skin tissue.
• Implement classical image processing techniques to process and analyze high volumes of messy imaging data into clear biological insights.
• Research, apply, fine-tune, and/or train supervised and unsupervised ML computer vision models for cell segmentation and tissue classification.

3. Code Implementation & Engineering Collaboration

• Develop robust, well-documented analysis scripts in Python. Contribute to and help maintain a modern git-ops repository.
• Prototype end-to-end workflows that process raw whole-slide images (WSI) into actionable data tables.
• Partner with the Data Engineering team and Devops to translate research-grade scripts into scalable, cloud-native, production-ready pipelines, ensuring models are performant and reproducible.

Required Qualifications

• Education: PhD or Master’s degree in Biomedical Engineering, Biophysics, Bioinformatics, Computer Science (with a focus on Image Analysis), or a related field.
• Experience: 3 years of experience in digital pathology or bioimage analysis, specifically working with ex-vivo tissue samples (skin experience is a major plus).
• Coding Proficiency: Expert-level skills in Python (NumPy, Scikit-Image, PyTorch/TensorFlow)
• Domain Knowledge: Strong understanding of histology, tissue morphology, and the chemical basis of common staining techniques.
• Tools: Experience with Digital Pathology software such as QuPath, HALO, Visiopharm, or CellProfiler.
• Fluent in Cloud Infrastructure/Cloud Computing and experience working on production-grade software projects: Docker, EKS, S3, Airflow, Github, K8s, Sagemaker