Machine Learning Scientist, Pathology Imaging

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

Passionate about precision medicine and advancing the healthcare industry?

Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.

We are seeking a Machine Learning Scientist to join our pathology imaging team. You will be part of a team of experts in machine learning, pathology, and computer vision that is building state-of-the-art machine learning solutions to accelerate and improve patient care. The successful candidate will lead the development of novel machine learning methods for analyzing pathology imaging data.

What you will do:

• Design novel machine and deep learning models applied to digital pathology images. 
• Collaborate closely with cross-functional teams (pathologists, biologists, engineers) to solve complex problems in translational science and computational pathology. 
• Provide technical leadership in machine and deep learning, driving and shaping research directions in digital pathology. 
• Contribute to and drive publications and present results at internal and external scientific conferences. 

Required qualifications:

• Ph.D. in Computer Science, Statistics, Applied Mathematics, Computational Biology, Physics, related technical field, or equivalent practical experience.
• Demonstrated experience with Python and analysis of image-like data.
• Fluent with at least one deep learning framework for neural networks (e.g. PyTorch or Tensorflow).
• Experience with engineering best practices for computational research (Docker, GIT, cloud computing, code review). 
• Experience with communicating insights and presenting concepts to diverse audiences.
• Strong publication record and experience contributing to research communities, including conferences like NeurIPS, ICML, ICLR, AISTATS, CVPR, ICCV, etc.

Preferred qualifications:

• Experience working with structured and unstructured data including genomic (e.g., NGS) or clinical (e.g., real-world evidence, trials) data.
• Strong database and SQL skills. 
• Intense curiosity about the biology of disease and eagerness to contribute to scientific and computational efforts.
• 2+ years of experience building and validating machine learning models on structured/unstructured data.