Machine Learning Engineer, Data Scientist

🕒 June 20

🌵 Arizona, California, +14 more states – Remote

🌵 Arizona – Remote 🏄 California – Remote ⛰️ Colorado – Remote 🐊 Florida – Remote 🌽 Illinois – Remote 🏖️ New Jersey – Remote 🗽 New York – Remote 🌲 North Carolina – Remote 🏈 Ohio – Remote 🍂 Massachusetts – Remote 🚗 Michigan – Remote ❄️ Minnesota – Remote 🏰 Missouri – Remote 🔔 Pennsylvania – Remote 🤠 Texas – Remote 🧀 Wisconsin – Remote

💵 $100.7k - $167.8k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

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Clinician Nexus

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2016

🏥 Healthcare

⚕️ Healthcare Insurance

📚 Education

💰 Seed Round on 2019-12

Healthcare • Healthcare Insurance • Education

Clinician Nexus is a platform that enables health systems to build a high-quality clinical workforce through transparency and insight into productivity, clinical practice management, and clinical learning. It provides tools for compensation management and student management, supporting over 50,000 clinicians and 143,500 students across 2,400+ clinical sites. Clinician Nexus is dedicated to helping healthcare organizations educate, engage, and retain their clinical workforce at every stage of their careers.

📋 Description

• 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 for data processing and model training, versioning, deployment, and monitoring • Evaluate the performance of machine learning models, including LLM-specific evaluation approaches, and tune models for optimal performance • Communicate findings, insights, and model performance to both technical and non-technical audiences • Continuously stay updated on the latest trends, technologies, and best practices

🎯 Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field • Bachelor with 5+ years of relevant experience • Master or higher with 3+ years of relevant experience • Fluent in Python (3+ years of coding experience) • Strong software development practices in Python, including writing maintainable, testable, production-ready code • Solid understanding of LLM architectures and Generative AI • Hands-on experience building and evaluating RAG pipelines • Experience with LLM orchestration frameworks (LangChain, LlamaIndex, or similar) • Proficiency in machine learning libraries such as Scikit-learn and PyTorch; and fundamental libraries such as NumPy and Pandas • Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and containerization tools (e.g., Docker) • Strong understanding of model evaluation metrics across traditional ML (e.g., accuracy, precision, recall, F1) and LLM-based systems (e.g., faithfulness, answer relevancy, hallucination detection) • Experience with model management tools such as MLFlow and the model development life cycle • Experience with version control tools such as Git • Proficiency in adapting SDLC best practices for code development and testing • Excellent problem-solving skills, analytical thinking, and the ability to work in a fast-paced environment • Strong communication skills and the ability to explain complex technical concepts to non-technical stakeholders.

🏖️ Benefits

• Competitive total compensation package • Medical and dental coverage at no premium cost for employees • 401(k) and profit-sharing retirement plans • Flexible spending accounts • Paid time off (PTO) • Company-paid holidays • Gender-neutral parental leave • Bereavement and pet leave • Continuing education and professional accreditation sponsorship • Life and AD&D insurance • Short- and long-term disability • Employee assistance program • Mental health support program • Additional perks