Data Scientist-Clinical Applications

Biolink360; Denver (CO); Hybrid; USD 140,000 - 180,000; Full time

Job summary

An established industry player is seeking a talented Data Scientist specializing in Machine Learning to join their innovative team. This hybrid role focuses on designing, developing, and deploying advanced machine learning models that enhance image analysis and processing. You will collaborate with a diverse team of experts to tackle real-world challenges in medical imaging and computer vision. If you're passionate about leveraging AI to drive next-generation imaging solutions, this opportunity offers a chance to make a significant impact in a forward-thinking environment that values creativity and innovation.

Qualifications

• Master's or Ph.D. in relevant fields required.
• Strong experience in machine learning and deep learning techniques.

Responsibilities

• Design and deploy advanced machine learning models for imaging.
• Collaborate with cross-functional teams to create innovative AI solutions.

Skills

Machine Learning

Deep Learning

Computer Vision

Python

Statistical Analysis

Ethical AI Considerations

Education

Master’s or Ph.D. in Computer Science

Master’s or Ph.D. in Data Science

Master’s or Ph.D. in Electrical Engineering

Tools

TensorFlow

PyTorch

Keras

OpenCV

PIL

skimage

Job description

Title: Data Scientist
Reports to: Director of Data Science
Salary Range: 140-180K
Location: Denver, CO (hybrid role)

This global company is a leading innovator in imaging technology, leveraging advanced machine learning and Artificial Intelligence to develop cutting-edge solutions in medical imaging and computer vision applications. We are hiring a talented Data Scientist specializing in Machine Learning to join the team and help drive the next generation of AI-driven imaging solutions.

Job Description:
This role involves machine learning for imaging applications. You will be responsible for designing, developing, and deploying advanced machine learning models that improve image analysis, recognition, and processing. You will work closely with cross-functional teams, including software engineers, imaging specialists, and product managers, to create innovative AI solutions.

Responsibilities:

1. Collaborate with ML scientists, data engineers, biostatisticians, regulatory experts, and senior leadership to understand clinical requirements, develop innovative solutions, and support product development and regulatory approvals.
2. Conduct analysis and validation of machine learning algorithms and prototypes.
3. Train and optimize deep learning models (CNNs, GANs, transformers, etc.) for high-performance imaging applications.
4. Preprocess and analyze large-scale imaging datasets to improve model accuracy and efficiency.
5. Collaborate with software engineers to integrate AI models into imaging platforms and production systems.
6. Conduct research on the latest machine learning techniques and apply them to real-world imaging challenges.
7. Evaluate and improve model performance using metrics such as precision, recall, F1-score, and computational efficiency.
8. Work with domain experts to understand imaging requirements and refine AI-driven solutions accordingly.
9. Document methodologies, experiments, and findings to contribute to the company's knowledge base.

Qualifications:

1. Master’s or Ph.D. in Computer Science, Data Science, Electrical Engineering, or a related field.
2. Strong experience in machine learning, deep learning, and computer vision techniques.
3. Proficiency in Python and machine learning frameworks such as TensorFlow, PyTorch, or Keras.
4. Experience with image processing libraries such as OpenCV, PIL, or skimage.
5. Experience handling large datasets, implementing ML pipelines, and visualizing analysis results.
6. Experience in medical imaging, industrial inspection, or remote sensing applications.
7. Familiarity with edge computing and AI model deployment on embedded systems.
8. Knowledge of statistical analysis, anomaly detection, and generative models.
9. Understanding of ethical AI considerations and bias mitigation in machine learning models.

To be considered, or obtain additional information, reach out to Jackie Cassidy at [e-mail]