Machine Learning Data Scientist, Pathology

Key Skills

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

Job Description

We are seeking an outstanding Machine Learning Data Scientist to take part in advancing state-of-the-art pathology image analysis. This scientist will be responsible for researching, implementing and characterizing novel ML and analysis techniques for large scale feature identification, segmentation and quantification, and image understanding, applying these to large datasets generated by newly developed microscopy systems from tissue specimens. Successful candidates will demonstrate proactivity, creativity, and an eagerness to push the boundaries in a dynamic and fast paced environment. Together with an interdisciplinary team of scientists, hardware and software engineers, medical professionals, business and strategy experts and regulatory specialists, this role will help develop technology to unlock new insights from tissue, revolutionizing understanding and treatment of diseases like cancer.

We partner with leading life science, medical devices, and government and academic organizations to enable fast development, meaningful advances, and deployment at scale. Our work spans many projects, including Immune Profiler, our deep immune molecular mapping platform; Virtual Stainer, our hyperspectral microscopy and unstained tissue predictive algorithms platform.

Responsibilities

• Build end to end machine learning systems on large scale imaging and clinical data.
• Research novel deep model architectures, read papers, implement and deploy them.
• Build models from inception to launch: exploratory models, feature engineering, deep model architectures, tuning, and model interpretation.
• Work with state-of-the-art ML techniques, including tools like TensorFlow and TPU.
• Communicate technical results with, and contribute new ideas to, a very collaborative and cross-functional team.

Qualifications

Minimum qualifications:

• PhD in either Computer Science or Machine Learning, or MS with 2+ years practical experience.
• Demonstrated track record, including peer-reviewed publications, of building and using deep learning models on large data, preferably images.
• Experience with Python, C++, and Tensorflow as well as basic computer vision and image processing methods.
• Experience with exploratory and statistical data analysis (such as hypothesis testing, linear models, multivariate analysis, predictive modeling and stochastic models).

Preferred qualifications:

• Demonstrated ability to design and implement novel experiments, algorithms and methods.
• Experience working with large-scale imaging data, including digital pathology data.
• Experience with developing on Google Cloud Platform.