Lead Data Scientist

Job description: We're looking for an experienced Lead Data Scientist to drive advanced healthcare-focused AI initiatives, from predictive models for patient care to NLP for clinical documentation. Join our team to help deliver innovative, impactful solutions for the healthcare sector. Role Overview: As a Lead Data Scientist in the healthcare domain, you will lead the development and deployment of AI-driven models that impact patient care, clinical decision support, operational optimization, and more. You will work closely with a cross-functional team of data scientists, engineers, clinicians, and healthcare professionals to design and implement data-driven solutions that improve healthcare outcomes. Your expertise in healthcare data, machine learning, and regulatory compliance will be key to your success in this role. What You’ll Do: • Lead AI Healthcare Initiatives: Oversee the design and deployment of machine learning models specifically for healthcare applications, such as predicting patient outcomes, improving clinical decision support, automating administrative tasks, and optimizing healthcare operations. • Healthcare Data Strategy & Optimization: Work with healthcare-specific data types (e.g., EHR/EMR, claims, sensor data) and design solutions that can handle and process large volumes of structured and unstructured healthcare data. Implement data pipelines and data lakes with a focus on healthcare privacy and security requirements. • Model Development & Evaluation: Design, implement, and evaluate machine learning models in the healthcare context (e.g., predictive models, NLP for clinical text mining, computer vision for medical imaging). Work with healthcare data scientists to ensure models are accurate, explainable, and clinically relevant. • Healthcare Compliance & Security: Ensure that all AI/ML models and solutions comply with healthcare regulations (e.g., HIPAA, 21 CFR Part 11, FDA guidance for AI in healthcare). Design solutions that protect patient privacy, secure sensitive health data, and comply with governance and security standards. • Clinical Collaboration: Partner with healthcare professionals, including doctors, nurses, and administrators, to ensure AI models are practically applicable and meet clinical and operational needs. Translate complex healthcare problems into actionable AI-driven solutions. • Research & Innovation in Healthcare AI: Stay up-to-date with the latest AI research and healthcare innovations. Contribute to the advancement of AI applications in healthcare, including personalized medicine, patient monitoring, and predictive diagnostics. • Operational AI Solutions for Healthcare: Develop and maintain AI-driven systems that integrate into hospital IT ecosystems (e.g., EHR/EMR systems, PACS systems). Focus on system reliability, uptime, and performance. • Healthcare Performance Metrics: Establish and monitor key performance indicators (KPIs) for AI models in healthcare settings, including patient outcomes, model interpretability, model drift, and performance over time. • Mentorship & Team Leadership: Lead and mentor a team of data scientists and AI engineers, helping them develop healthcare-specific expertise and ensuring that all team members adhere to best practices for model development and deployment in the healthcare context. • Healthcare-Specific AI Tools & Frameworks: Implement and leverage industry-standard healthcare tools, frameworks, and datasets (e.g., FHIR, OHDSI, MIMIC-III, UMLS) for model development and validation. Key Responsibilities: • Healthcare AI Model Development: Build and deploy machine learning models to address key healthcare problems, such as disease prediction, patient risk assessment, clinical decision support, and operational efficiency. • Healthcare Data Integration: Collaborate with data engineers to manage complex healthcare datasets, ensuring data quality, integrity, and compliance with healthcare regulations. • Regulatory Compliance & Privacy: Ensure that all AI and data science work aligns with industry regulations such as HIPAA, PII/PHI protection, and FDA guidance on medical AI/ML systems. Implement privacy-preserving techniques (e.g., differential privacy, federated learning). • Model Evaluation in Healthcare Contexts: Use clinical, operational, and ethical criteria to evaluate models, ensuring that they are safe for deployment in a clinical or healthcare setting. Develop robust evaluation strategies that include real-world testing and validation. • Cross-Functional Collaboration: Partner with healthcare stakeholders (e.g., clinicians, administrators, IT teams) to ensure AI solutions are feasible, scalable, and aligned with business goals. Provide ongoing education about AI capabilities and limitations. • Healthcare-Specific Reporting & Documentation: Create model documentation and reports that meet healthcare industry standards for explainability, auditability, and regulatory compliance. • AI for Healthcare Operations: Optimize AI-driven tools for operational use cases such as improving patient flow, resource allocation, and staffing, with an eye toward reducing costs and improving efficiency. Required Qualifications: • 8+ years of experience in data science, machine learning, and AI, with a proven track record in healthcare applications. • 3+ years of experience leading data science teams and delivering AI/ML projects in the healthcare industry. • Strong expertise in handling healthcare-specific data (e.g., electronic health records, medical imaging, claims data, clinical data). • Expertise in machine learning algorithms (e.g., supervised/unsupervised learning, deep learning, NLP, computer vision) and their application to healthcare problems. • In-depth understanding of healthcare regulations (e.g., HIPAA, PII/PHI, FDA regulations for medical AI/ML systems). • Experience with cloud platforms (AWS, Azure) and healthcare-focused tools (e.g., FHIR, OHDSI, MIMIC-III, HL7). • Excellent communication skills, with the ability to communicate complex healthcare AI concepts to non-technical stakeholders, including clinicians and hospital administrators. • Strong experience in AI model deployment, performance monitoring, and ongoing model improvement. • Ability to mentor junior data scientists and help them build healthcare-specific AI expertise. Preferred Qualifications: • Familiarity with healthcare AI research areas, such as personalized medicine, medical imaging, or clinical decision support. • Advanced degree (PhD or Master’s) in Data Science, Computer Science, Statistics, Biomedical Engineering, or a related field. • Experience with federated learning, privacy-preserving machine learning, or other methods for handling sensitive healthcare data. • Previous experience working with healthcare organizations (hospitals, medical device companies, health tech startups).