Data Scientist ML, Docker, ECS, Python *** Direct end client ***

Company: Projas Technologies, LLC
Location: South San Francisco, California, United States
Type: Full-time
Posted: 03.APR.2021

Summary

Required skills Strong experience in data science and Machine learning Convert existing algorithms to containerized processing units (docke...

Description

Required skills

Strong experience in data science and Machine learning
Convert existing algorithms to containerized processing units (docker) using python
Deploy Docker containers to ECS (AWS) and Kubernetes
Write Python code to execute containerized machine learning models
Prior experience in processing DICOM and non DICOM images
Machine learning models for life sciences industry

In-depth knowledge and coding experience in Python (polyglot in multiple programming languages a plus). Hands-on skills in Data Science packages, for instance Pandas, Scikit-learn, and/or numpy, a must.

E xtensive experience with commonly used Deep Learning models (2d/3d CNN, LSTM/GRU, etc), modern DL architectures (Resnet, U-net, etc), and frameworks (Tf, pytorch, keras, etc). Hands-on on other ML algorithms (RF, GBM, etc) a plus.

Familiarity with advances in AI research and related applications in medical imaging, and/or computer vision (eg video).

Technical and organizational skills/experience to lead complex, end-to-end ML/DL/AI projects, including typical project stages such as: data engineering, computing/storage resource budgeting, model training, model selection, model evaluation, and communication with other stakeholders.

Fluent in using scientific computing environment e.g. unix / linux shell in a HPC cluster on premise or in cloud, to accomplish common development tasks (eg. editing, testing, efficient debugging, etc.) Hands-on experience with productivity toolchains (eg JIRA, enterprise git.)

Understand the practical aspect of the mathematical foundation of ML, in particular optimization (first order method eg gradient descent, second order method eg Newton-Raphson, why in DL first order is dominant). Understand the practical aspect of statistics (population vs sample, different sampling techniques, etc)

PhD or MS in relevant quantitative field (CS, EE, Physics, Mathematics, Statistics, etc.), and/or adv. Life Sciences degree with significant computational experience

>3yr post-graduate work-experience in fields such as engineering, research, or product development with responsibilities relevant to position.

Publications in the areas of Deep-/Machine Learning, and/or Statistics a plus.

Solid understanding of medical image data formats (eg DICOM)

Excellent communication skills

Ability to multitask and prioritize while maintaining efficiency and quality of work

Internally motivated with a commitment to accuracy and quality

- provided by Dice

 
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