$ cat jobs/machine-learning-engineer-data-scientist-clinician-nexus-6f9dbfd5f5cf.json
Machine Learning Engineer / Data Scientist
This is a remote role; however, we only operate in the following states: AZ, CA, CO, FL, GA, IL, IN, MA, MI, MN, MO, NJ, NY, NC, OH, PA, TX and WI. ABOUT US AND ABOUT YOU Clinician Nexus enables health care organizations to build thriving clinician teams with industry-leading technology products, workforce and compensation analytics, and automated workflow solutions. Backed by extensive technical expertise and industry-leading data, we deliver innovative approaches to help clients plan, educate, and engage their clinical workforce at every stage of the lifecycle. We are committed to providing our clients with outstanding guidance and support as they work to shape the future of health care. JOB SUMMARY We are seeking a highly skilled and motivated Machine Learning Engineer to join our growing Data Science team. You will develop and deploy cutting-edge machine learning models and advanced data analytics solutions to solve real-world problems. You will collaborate with cross-functional teams (Product, Data Platform, DevOps, Software Engineering, etc.) to extract meaningful insights from data, develop scalable machine learning solutions, and help drive data-informed decision-making. PRIMARY ACCOUNTABILITIES Design, develop, and deploy ML solutions ranging from traditional ML applications (classification, clustering, recommendations) to LLM-based systems, including document parsing, data extraction, RAG pipelines, and LLM agents. Write clean, maintainable, production-quality Python code that integrates smoothly with existing engineering and deployment infrastructure. Work with large datasets to clean, preprocess, and analyze data, ensuring data quality and integrity. Implement and optimize algorithms using best practices in machine learning, deep learning, and statistical analysis. Collaborate with business stakeholders to understand requirements and deliver data-driven solutions that provide actionable insights. Develop and maintain scalable pipelines and infrastructure
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