"Alerted.org

Strategy and Leadership

+ Establish and manage an industry standard AI and Data framework.

+ Develop and Execute Strategy: Define the enterprise-wide AI and data strategy, roadmap, and vision, ensuring alignment with overall business objectives and measurable outcomes.

+ Team Leadership: Lead, mentor, and grow high-performing teams of business intelligence, machine learning engineers (MLEs), and data engineers.

+ Cultural Transformation: Foster a data-driven culture by promoting data literacy, AI and data understanding, and responsible practices across the organization.

+ Stakeholder Collaboration: Partner with C-suite executives and cross-functional leaders and vendor partners to identify and embed AI/data-driven insights into core business processes, automation, and digital transformation efforts.

Technical Oversight and Implementation

+ AI/ML Development: Oversee the research, development, deployment, and monitoring of AI/Machine Learning (ML) models, including generative AI, deep learning, and predictive analytics.

+ Data Architecture: Provide technical guidance on the design and maintenance of scalable and efficient data infrastructure, data pipelines, data warehouses, and data lakes.

+ Innovation: Spearhead AI-driven business models, products, and services to create a competitive advantage, continuously evaluating and integrating new AI/ML technologies and platforms.

+ Operational Excellence: Establish best practices for MLOps, DataOps, and model governance to ensure solutions are accurate, reliable, and scalable in production environments.

Governance and Ethics

+ Data Governance: Establish and enforce governance mechanisms to ensure the integrity, quality, security, and availability of data assets.

+ Ethical AI: Ensure the ethical and responsible use of AI, including bias mitigation, transparency, and explainability in all AI applications.

+ Compliance: Oversee compliance with all relevant data privacy laws and AI regulatory requirements (e.g., GDPR, CCPA, or emerging AI acts).

+ Risk Management: Identify and mitigate technical, ethical, and operational risks associated with AI and data initiatives.

KNOWLEDGE/EXPERIENCE:

Technical Expertise (Hard Skills)

+ Advanced AI/ML Knowledge: Deep understanding and practical experience with machine learning, deep learning, statistical modeling, and algorithms.

+ Programming & Tools: Proficiency in languages like Python and R, as well as hands-on experience with relevant AI frameworks (e.g., TensorFlow, PyTorch) and cloud AI platforms (AWS, Azure, GCP).

+ Data Ecosystem: Strong knowledge of data engineering concepts, including ETL processes, data integration, data warehousing, and big data technologies (e.g., Snowflake, Apache Spark).

EDUCATION: A Bachelor's or Master's degree (often a Ph.D. is preferred for highly technical roles) in Data Science, Computer Science, AI, Engineering, or a related quantitative field.

LICENSE/CERTIFICATION/REGISTRY: None

Attributes: Strategic Vision: Ability to translate complex technical concepts into clear, concise business strategies that drive tangible results. Leadership and Management: demonstrate ability to lead, mentor, and managing technical teams and working with executive leaders. Communication: Excellent verbal and written communication skills to effectively present complex technical insights to both technical teams and non-technical executive stakeholders. Business Acumen: Strong understanding of the healthcare industry and business drivers to ensure AI initiatives deliver maximum commercial impact. Change Management: Ability to drive significant organizational change and foster the adoption of new data-driven methodologies.

This position has remote available options