Responsibilities:

• Analyze Data: Collaborate with cross-functional teams to understand data requirements and identify relevant data sources. Analyze and preprocess data to extract valuable insights and ensure data quality.
• Model Development: Develop and implement machine learning models to address patient scheduling challenges. Utilize advanced algorithms and techniques to optimize scheduling processes and improve efficiency.
• Hypothesis Testing: Test hypotheses and validate assumptions through rigorous experimentation and analysis. Conduct feasibility checks to assess the viability and effectiveness of proposed solutions before deployment.
• Solution Design: Work closely with stakeholders to define requirements and translate business objectives into technical specifications. Design scalable and robust AI solutions tailored to meet specific client needs.
• Evaluation and Optimization: Evaluate model performance using appropriate metrics and iterate on solutions to enhance performance and accuracy. Continuously optimize algorithms and models to adapt to evolving business requirements.
• Collaboration: Collaborate with data engineers, software developers, and domain experts to integrate AI/ML solutions into existing systems and workflows. Ensure seamless deployment and integration of solutions within client environments.
• Documentation and Reporting: Document methodologies, findings, and outcomes in clear and concise reports. Communicate results effectively to technical and non-technical stakeholders, and provide actionable insights for decision-making.

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related field. Ph.D. preferred.
• Proven experience in data analysis, machine learning, and AI model development, preferably in the healthcare domain.
• Proficiency in programming languages such as Python, R, and familiarity with python-notebooks for model development and libraries such as TensorFlow, PyTorch, scikit-learn, etc.
• Strong understanding of statistical methods, data mining techniques, and predictive modeling.
• Experience with data preprocessing, feature engineering, and model evaluation.
• Excellent problem-solving skills and the ability to think critically and analytically.
• Strong communication and interpersonal skills, with the ability to collaborate effectively in a team environment.
• Proven track record of delivering high-quality solutions on time and within budget.
• Knowledge of healthcare standards and regulations (e.g., HIPAA) is a plus.
• Ability to adapt to a fast-paced, dynamic work environment and learn new technologies quickly.