Responsible for applying the latest in advanced analytics and ML technology to help CHS and the facilities we serve realize the potential of big data and automation in the cloud.
Community Health Systems is one of the nation’s leading healthcare providers. Developing and operating healthcare delivery systems in 47 distinct markets across 16 states, CHS is committed to helping people get well and live healthier. CHS operates 79 acute-care hospitals and more than 1,000 other sites of care, including physician practices, urgent care centers, freestanding emergency departments, occupational medicine clinics, imaging centers, cancer centers and ambulatory surgery centers.
Responsible for applying the latest in advanced analytics and ML technology to help CHS and the facilities we serve realize the potential of big data and automation in the cloud. Builds ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques.
Essential Duties and Responsibilities:
- Provides machine-learning capabilities to monitor spikes and drops in usage, as well as lead discussions for architectural reviews for demand and capacity planning and management.
- Works closely with Data scientists to train data models for integration with the powerful GCP data and analytics engine.
- Participates in model design, optimization, testing, quality assurance, and defect resolution.
- Leverages general use APIs to include but not limited to Vision, Video Intelligence and Natural Language.
- Uses AutoML for custom needs like custom labels.
- Delivers and orchestrates machine learning infrastructure within testing and production environments.
- Collaborates with Engineers and Scientists to architect and implement a shared vision.
Required Education: Bachelor's degree in Computer Science, or related technical field.
Preferred Education: Master’s degree in Computer Science, or related technical field.
- Minimum 5 years of hands on experience with Cloud using Google Cloud Platform (GCP), Microsoft Azure or Amazon Web Services (AWS)
- Hands on experience training and retraining data models to improve performance, using accelerators such as TPUs and GPUs
- Should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation, as well as familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the GCP ML Engineer will design and create scalable solutions for optimal performance in the cloud.
- Experience performing distributed training and serving and a wide range of available tools (such as What-If tool)
- Hands on experience with TensorFlow and BigQuery ML
- Understanding of machine learning and advanced analytics and the application thereof in cloud-native environments.
- Self-starter with an in-depth hands-on work experience with multiple large-scale implementations of Google Cloud Platform
- Experience designing and implementing distributed software systems (e.g. Java, C++, or Python)
- Strong understanding and experience with common security libraries, security controls, and common security flaws.
- Knowledge of and experience designing and managing Cloud controls
- Deep AI/ML implementation experience across
- Python development experience training models
Required License/Registration/Certification: Google AI Platform, ML/AI certification or study (any)
Preferred License/Registration/Certification: GCP Professional ML Engineer Certification
Computer Skills Required: Python, TensorFlow, Google AI Platform
In order to successfully perform this job, with or without a reasonable accommodation, the following are outlined below:
- The Employee is required to read, review, prepare and analyze written data and figures, using a PC or similar, and should possess visual acuity.
- The Employee may be required to occasionally climb, push, stand, walk, reach, grasp, kneel, stoop, and/or perform repetitive motions.
- The Employee is not substantially exposed to adverse environmental conditions and; therefore, job functions are typically performed under conditions such as those found within general office or administrative work.