Image Analysis / Machine Learning Scientist

Image Analysis / Machine Learning Scientist
Position Summary
The Image Analysis / Machine Learning Scientist will be responsible for the design, development, optimization, validation, and deployment of advanced machine learning and computer vision solutions that support computational pathology, biomarker discovery, and translational research initiatives. This role combines expertise in artificial intelligence, digital pathology, image analytics, and data science to generate quantitative insights from complex histological and biomedical imaging datasets.
The successful candidate will develop innovative image analysis algorithms, deep learning models, and automated workflows to support tissue characterization, biomarker quantification, cellular phenotyping, and predictive modeling. Working closely with computational pathologists, pathologists, translational scientists, bioinformaticians, and software engineers, this individual will help drive the adoption of AI-enabled methodologies that improve scientific decision-making and accelerate research outcomes.
The role requires hands-on experience applying machine learning, deep learning, and computer vision techniques to large-scale biomedical image datasets, including whole-slide pathology images, microscopy images, and multiplex imaging platforms. The candidate must be capable of translating scientific objectives into robust analytical solutions while ensuring accuracy, reproducibility, and scalability. Key Responsibilities
Machine Learning & Artificial Intelligence Development

• Design, develop, train, and deploy machine learning and deep learning models for computational pathology and biomedical imaging applications.
• Develop supervised, unsupervised, and semi-supervised learning approaches for image classification, segmentation, object detection, and feature extraction.
• Optimize model performance through hyperparameter tuning, architecture selection, and performance evaluation.
• Implement explainable AI methodologies to support scientific interpretation and regulatory transparency.
• Evaluate emerging AI technologies and assess applicability to computational pathology challenges.
• Develop scalable machine learning workflows capable of supporting high-throughput image analysis environments.

Computer Vision & Image Analysis

• Design and implement advanced image analysis pipelines for histopathology and digital pathology applications.
• Develop algorithms for tissue segmentation, cellular detection, phenotyping, spatial analysis, and morphological characterization.
• Apply computer vision techniques to extract biologically relevant features from histological and microscopy images.
• Develop automated workflows to process whole-slide images and large imaging datasets.
• Improve image quality, artifact detection, normalization, and preprocessing methodologies.
• Support quantitative analysis of tissue biomarkers and disease-related features.

Computational Pathology Support

• Collaborate with computational pathologists to develop AI-driven pathology solutions.
• Support development and optimization of digital pathology workflows for biomarker discovery and translational research.
• Analyze whole-slide imaging data using commercial and open-source pathology platforms.
• Develop image analysis approaches for:
  • Immunohistochemistry (IHC)
  • Immunofluorescence (IF)
  • Multiplex imaging
  • Spatial biology applications
  • Tissue morphology analysis
• Contribute to validation and deployment of computational pathology algorithms.

Biomarker Discovery & Translational Research

• Support identification and validation of image-derived biomarkers.
• Develop analytical methods to quantify biomarker expression and spatial relationships within tissues.
• Collaborate with translational scientists to integrate image-derived data with molecular and clinical datasets.
• Generate quantitative insights that support preclinical and clinical research programs.
• Assist in developing predictive models associated with disease progression, treatment response, and patient stratification.

Model Validation & Performance Assessment

• Design and execute validation studies for machine learning models and image analysis workflows.
• Evaluate model accuracy, precision, sensitivity, specificity, robustness, and reproducibility.
• Develop benchmark datasets and performance metrics for algorithm evaluation.
• Conduct statistical analyses to assess model reliability and scientific validity.
• Troubleshoot model failures, data quality issues, and workflow bottlenecks.
• Maintain documentation supporting validation and regulatory review requirements.

Data Management & Workflow Automation

• Develop automated image processing and analytical workflows.
• Integrate machine learning pipelines into existing computational pathology infrastructures.
• Support metadata integration and linkage between image-derived outputs and study datasets.
• Collaborate with software engineers and data scientists to improve system scalability and performance.
• Contribute to cloud-based and high-performance computing implementations when required.

Scientific Communication & Collaboration

• Present technical findings to scientific teams, leadership, and project stakeholders.
• Prepare scientific reports, validation documentation, technical protocols, and presentations.
• Participate in cross-functional project meetings and governance reviews.
• Collaborate with pathologists, computational scientists, software engineers, statisticians, and translational researchers.
• Contribute to publications, conference presentations, and intellectual property development.

Education Requirements
Required

• Master's degree in:
  • Computer Science
  • Artificial Intelligence
  • Machine Learning
  • Biomedical Engineering
  • Computational Biology
  • Data Science
  • Applied Mathematics
  • Bioinformatics
  • Related quantitative discipline

Preferred

• PhD in:
  • Computer Science
  • Artificial Intelligence
  • Machine Learning
  • Biomedical Engineering
  • Computational Biology
  • Data Science
  • Related field

Experience Requirements
Required

• 5+ years of experience in machine learning, artificial intelligence, computer vision, or image analysis.
• Minimum 3 years supporting biomedical imaging, digital pathology, healthcare AI, or related applications.
• Experience developing machine learning solutions from concept through deployment.

Preferred

• Experience supporting pharmaceutical, biotechnology, medical device, CRO, or academic research organizations.
• Experience developing AI solutions for pathology, oncology, immunology, or translational medicine programs.
• Experience working within regulated research or healthcare environments.

Required Technical Expertise
Machine Learning & Artificial Intelligence

• Deep learning model development
• Neural network architecture design
• Convolutional Neural Networks (CNNs)
• Vision Transformers (ViTs)
• Transfer learning
• Object detection models
• Image classification
• Semantic and instance segmentation
• Predictive modeling
• Explainable AI (XAI)

Computer Vision

• Feature extraction
• Pattern recognition
• Image registration
• Object detection
• Image segmentation
• Morphological analysis
• Spatial analysis
• Image enhancement and normalization

Computational Pathology

• Histopathology image analysis
• Whole-slide image processing
• Tissue segmentation
• Cell detection and phenotyping
• Biomarker quantification
• Spatial biology analysis
• Digital pathology workflows

Software & Programming

• Python
• TensorFlow
• PyTorch
• OpenCV
• Scikit-learn
• NumPy
• Pandas
• Jupyter Notebooks
• Git

Image Analysis Platforms

• Halo AI
• QuPath
• Visiopharm (preferred)
• CellProfiler (preferred)
• ImageJ/Fiji

Statistical & Analytical Methods

• Statistical modeling
• Hypothesis testing
• Experimental design
• Model validation
• Performance benchmarking
• Data visualization

Preferred Qualifications

• Experience with multiplex imaging technologies.
• Experience supporting oncology, immunology, inflammation, or vaccine research.
• Familiarity with spatial transcriptomics and spatial biology platforms.
• Experience working with cloud computing platforms such as AWS, Azure, or Google Cloud.
• Experience deploying machine learning models into production environments.
• Publications, patents, or significant scientific contributions in AI, machine learning, or computational pathology.
• Knowledge of regulatory expectations related to AI-driven healthcare and life sciences applications.

Estimated Min Rate: $49.00
Estimated Max Rate: $70.00


What’s In It for You?
We welcome you to be a part of the largest and legendary global staffing companies to meet your career aspirations. Yoh’s network of client companies has been employing professionals like you for over 65 years in the U.S., UK and Canada. Join Yoh’s extensive talent community that will provide you with access to Yoh’s vast network of opportunities and gain access to this exclusive opportunity available to you. Benefit eligibility is in accordance with applicable laws and client requirements. Benefits include:

• Medical, Prescription, Dental & Vision Benefits (for employees working 20+ hours per week)
• Health Savings Account (HSA) (for employees working 20+ hours per week)
• Life & Disability Insurance (for employees working 20+ hours per week)
• MetLife Voluntary Benefits
• Employee Assistance Program (EAP)
• 401K Retirement Savings Plan
• Direct Deposit & weekly epayroll
• Referral Bonus Programs
• Certification and training opportunities

Note: Any pay ranges displayed are estimations. Actual pay is determined by an applicant's experience, technical expertise, and other qualifications as listed in the job description. All qualified applicants are welcome to apply.

Yoh, a Day & Zimmermann company, is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

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