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.

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.