The Data Science team is pioneering the machine learning product offerings for CareRev. We are looking for passionate machine learning leaders that want the opportunity to own, define, and shape the future of machine learning for CareRev and ultimately for patient care. Your focus will be to work closely with Data Engineers and DevOps Engineers to begin the build-out phase of our end-to-end machine learning pipelines using the latest technologies including Deep Learning, NLP, and OCR. This role reports to the Director of Data Science in an individual-contributor capacity.

What You’ll Do:  

• Become a thought leader for machine learning best practices when it comes to development, versioning, deployment, testing, and evaluation to provide new ideas to the company.
• Create and define scalable machine learning pipelines using microservices methodologies while writing production code to perform research, training, tuning, serving, CI/CD, and monitoring.
• Research and develop Deep Learning technologies to improve the performance and evaluation of unsupervised/supervised models, including LTSM, RNN, CNN, RL, and Bayesian Deep Learning.
• Utilize Tensorflow, Pytorch, Spark, AWS Sagemaker, GPU’s, NoSQL/SQL and to develop and deploy machine learning models into a production environment
• Collaborate and connect across functions to improve and learn from team members in a fast-pace environment.

What We’re Looking For:  

We understand not all people fit the mold, so if you think you do not fit the mold, yet up to the challenge, please apply.

• 4 to 6 years writing machine learning production code for customers
• Experience with creating end-to-end machine learning pipelines using containerization in AWS to improve client-facing products
• Experience with Tensorflow, Pytorch, Airflow, Kubernetes, Docker, Microservices,REST API’s, Python, NoSQL/SQL, and Snowflake
• Experience with designing database schemas for machine learning pipelines to track model performance and to create feature stores
• Experience mentoring external and internal teams in machine learning best practices
• MLOps experience desired
• Start-up experience and non-traditional backgrounds are a big plus
• No travel required