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This is a remote position.

Key Responsibilities

1. Research & Model Development
   

   • Design, develop, and optimize machine learning models to enhance healthcare delivery and decision-making.
     
   • Conduct research to identify and implement state-of-the-art algorithms in areas such as natural language processing (NLP), predictive analytics, and recommendation systems.
     

2. Data Analysis & Feature Engineering
   

   • Collaborate with data engineers and analysts to preprocess, clean, and prepare large, complex datasets.
     
   • Identify and extract meaningful features to improve model performance and scalability.
     

3. Model Deployment & Monitoring
   

   • Deploy and integrate machine learning models into production environments, ensuring robustness and reliability.
     
   • Monitor model performance and iteratively improve using real-world feedback and data.
     

4. Collaboration & Leadership
   

   • Work closely with cross-functional teams, including engineering, product, and clinical experts, to align AI solutions with business objectives.
     
   • Mentor junior data scientists and machine learning engineers, fostering a culture of innovation and learning.
     

5. Experimentation & Continuous Improvement
   

   • Conduct experiments to evaluate and compare algorithms, models, and techniques.
     
   • Stay updated on the latest trends and advancements in machine learning and AI to drive innovation.
     

Requirements

Qualifications

• Ph.D. or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.
  
• 5+ years of experience in machine learning, data science, or AI research and development.
  
• Strong expertise in Python and libraries such as TensorFlow, PyTorch, or Scikit-learn.
  
• Proven track record of developing and deploying machine learning models in production environments.
  
• Deep understanding of algorithms, statistics, and advanced techniques in machine learning and AI.
  

Preferred Skills

• Experience with NLP, computer vision, or time-series analysis.
  
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud for model deployment and scalability.
  
• Knowledge of healthcare data standards and compliance (e.g., HIPAA, FHIR).
  
• Strong communication and presentation skills to share findings with technical and non-technical stakeholders.
  

Benefits