Data Scientist II, Deep Learning & Image Analysis

About the role

The Allen Institute accelerates science for a healthier world through large-scale research designed to answer complex questions in biology; its multi-disciplinary teams generate foundational knowledge, tools, and data to understand how the brain, cells, and immune system work, sharing work openly so others can build on it and ask bigger questions. The goal of Cellscapes is to create the first holistic framework to predict how cells self-organize into complex cell communities and tissues to program a new kind of synthetic tissue for focused questions about cell behavior, including responses to drug interventions. The Computational Cell Science team develops scalable, quantitative image-based frameworks for analyzing cell organization, activity, and function. As a Data Scientist specializing in computer vision, you will work alongside experimental and computational scientists, developing and implementing machine learning and deep learning methods for 3D timelapse microscopy images and owning defined components of the analysis pipeline end to end, with technical decisions guided by senior team members. The role sits at the intersection of microscopy, stem cell biology, machine learning, and scientific software, involving large‑scale microscopy datasets, state‑of‑the‑art models on dedicated HPC infrastructure, and open‑source releases used by external researchers.

Responsibilities

• Design and implement ML/DL pipelines for microscopy image analysis, in collaboration with senior data engineers and scientists
• Preprocess and clean large-scale microscopy datasets, ensuring data quality and integrity for downstream analysis
• Develop, optimize, and fine-tune models, from classical algorithms through DL architectures, for standard image processing tasks such as segmentation, feature extraction, and classification
• Integrate and scale image analysis protocols into high-throughput computational pipelines, working with cross-functional teams
• Participate in code reviews and contribute to efficient, maintainable, well-documented codebases
• Maintain reproducible analysis records; version-controlled code, documented parameters, traceable results, and coordinate with experimental scientists on data collection and study design
• Track developments in machine learning and image analysis and evaluate promising methods against our data
• Adhere to SOPs, GLPs and regulatory requirements

Requirements

• Bachelor's degree in computer science, engineering, physics, data science, computational biology, or a related quantitative field
• 1+ year of relevant experience
• Demonstrated experience developing ML/DL models for segmentation, classification, or feature extraction; from research, internship, coursework, or professional work
• Proficiency in Python, including scientific and data manipulation libraries (e.g. NumPy, scikit-image)
• Working experience with a deep learning framework such as PyTorch or TensorFlow

Qualifications

• Master’s degree with 2+ years of relevant experience in computer science, engineering, physics, data science, computational biology, or a related quantitative field; or a Bachelor's degree with 4+ years of relevant experience in one of these fields
• Experience with microscopy image data, particularly 3D or timelapse
• Experience with classical machine learning methods (e.g. SVM, random forests) alongside deep learning approaches
• Experience developing or contributing to open-source scientific tools or packages
• Familiarity with software engineering practice: