Strong foundation in Computer Science and Algorithms, with experience designing efficient, scalable solutions.
Proficiency in Pattern Recognition and Statistics to build robust, data-driven models for complex healthcare use cases.
Hands-on experience with Neural Networks and modern machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Advanced programming skills in languages such as Python or Java, and familiarity with software engineering best practices (version control, testing, CI/CD).
Experience deploying models to production, including model monitoring, performance tuning, and lifecycle management.
Knowledge of data privacy and security principles, with the ability to work within regulated environments (e.g., healthcare, HIPAA-equivalent standards).
Strong analytical, problem-solving, and communication skills, with the ability to collaborate effectively in multidisciplinary teams.
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field; equivalent practical experience considered.
Prior experience in healthcare, health tech, or working with clinical data is an advantage.