Machine Learning Professional

As a Machine Learning / Data Science Expert, your role involves evaluating, validating, and operationalizing AI/ML models for real-world healthcare applications to ensure accuracy, fairness, and compliance. Key Responsibilities: - Review AI solutions, training datasets, and algorithm documentation. - Validate and benchmark model performance and fairness metrics. - Ensure compliance with data security and privacy standards. - Develop and test data pipelines to support pilot deployments. - Conduct hands-on validation of AI outputs against clinical decisions. - Support field teams in interpreting model outputs. - Collaborate with clinical and operations experts to adapt models to real-world contexts. - Contribute to capacity-building and documentation of AI evaluation methods. Qualification Required: - MS/PhD in Computer Science (preferred) OR a Postgraduate Diploma in Data Science combined with an undergraduate degree in Engineering, Statistics, Mathematics, or related discipline. Specific Skills: - Strong organizational and communication skills to work across diverse technical, clinical, and research teams. - Excellent project management and organizational skills: meticulous attention to detail, ability to work independently, manage multiple tasks efficiently. - Demonstrated ability to manage relationships with partner organizations. - Willingness to frequently travel to study sites to support pilot deployments. Mandatory Experience: - Minimum 3 years of experience in AI/ML model development, validation, and evaluation with large, real-world datasets. - Hands-on experience building and validating AI/ML models in predictive modeling, classification, natural language processing, or computer vision applications. - Experience in applying fairness, bias, and equity assessments in ML models. - Familiarity with data management, ML pipeline development, and IT system integration processes. - Knowledge of model explainability and interpretability tools. - Knowledge of data security, privacy, and regulatory compliance requirements. Please note that the job description does not include any additional details about the company. As a Machine Learning / Data Science Expert, your role involves evaluating, validating, and operationalizing AI/ML models for real-world healthcare applications to ensure accuracy, fairness, and compliance. Key Responsibilities: - Review AI solutions, training datasets, and algorithm documentation. - Validate and benchmark model performance and fairness metrics. - Ensure compliance with data security and privacy standards. - Develop and test data pipelines to support pilot deployments. - Conduct hands-on validation of AI outputs against clinical decisions. - Support field teams in interpreting model outputs. - Collaborate with clinical and operations experts to adapt models to real-world contexts. - Contribute to capacity-building and documentation of AI evaluation methods. Qualification Required: - MS/PhD in Computer Science (preferred) OR a Postgraduate Diploma in Data Science combined with an undergraduate degree in Engineering, Statistics, Mathematics, or related discipline. Specific Skills: - Strong organizational and communication skills to work across diverse technical, clinical, and research teams. - Excellent project management and organizational skills: meticulous attention to detail, ability to work independently, manage multiple tasks efficiently. - Demonstrated ability to manage relationships with partner organizations. - Willingness to frequently travel to study sites to support pilot deployments. Mandatory Experience: - Minimum 3 years of experience in AI/ML model development, validation, and evaluation with large, real-world datasets. - Hands-on experience building and validating AI/ML models in predictive modeling, classification, natural language processing, or computer vision applications. - Experience in applying fairness, bias, and equity assessments in ML models. - Familiarity with data management, ML pipeline development, and IT system integration processes. - Knowledge of model explainability and interpretability tools. - Knowledge of data security, privacy, and regulatory compliance requirements. Please note that the job description does not include any additional details about the company.