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Image Analysis / Machine Learning Scientist
Yoh,-A-Day-
- King of Prussia, Pennsylvania, United States
- King of Prussia, Pennsylvania, United States
Über
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.
Benefits
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
Estimated Minimum Rate $49.00
Estimated Maximum Rate $70.00
Legal / Compliance 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.
For California applicants, qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. All of the material job duties described in this posting are job duties for which a criminal history may have a direct, adverse, and negative relationship potentially resulting in the withdrawal of a conditional offer of employment.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Visit https://www.yoh.com/applicants-with-disabilities to contact us if you are an individual with a disability and require accommodation in the application process.
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Sprachkenntnisse
- English
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