Remote Senior Machine Learning Engineer - Real World Evidence - United States

Description:

• Develop and deploy cutting-edge machine learning models and algorithms to analyze Real-World Evidence (RWE) data in the healthcare industry.
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
• Preprocess and clean large-scale RWE data to ensure quality and integrity.
• Evaluate, select, train, fine-tune, and validate machine learning models using state-of-the-art methodologies.
• Optimize machine learning models for scalability, performance, and accuracy.
• Monitor and maintain deployed machine learning models for ongoing performance and relevance.
• Stay updated with the latest trends in machine learning and real-world evidence.
• Communicate findings and insights to technical and non-technical stakeholders.

Requirements:

• Bachelor's degree in computer science, data science, or related field; advanced degree preferred.
• Minimum 4 years of experience in machine learning engineering or data science, focusing on healthcare and real-world evidence (RWE).
• Strong knowledge of machine learning algorithms, statistical modeling, and data mining techniques.
• Proficiency in Python or R for data preprocessing, analysis, and model implementation.
• Experience with TensorFlow, PyTorch, or scikit-learn for machine learning.
• Understanding of database systems and SQL for data manipulation.
• Experience with big data technologies and distributed computing frameworks is a plus.
• Strong problem-solving and analytical skills.
• Excellent communication and collaboration skills.
• Preferred experience in the healthcare industry and familiarity with healthcare data standards.

Benefits:

• Opportunity to work with a world-leading life science company.
• Engage in cutting-edge machine learning projects in the healthcare industry.
• Collaborate with cross-functional teams to solve complex problems.
• Stay updated with the latest trends and advancements in machine learning and real-world evidence.
• Competitive salary and benefits package.
• Professional development opportunities.