Director of AI & Adv Analytics

Provides strategic and technical leadership for the organization's AI, ML, and advanced analytics capabilities. This role defines the vision, roadmap, and standards for AI-driven innovation, leads a team of AI/ML engineers and data scientists, and partners with business and technology leaders to deliver scalable solutions that generate insights, improve decision-making, optimize operations, and create measurable business value.

Essential Functions

The list of essential functions, as outlined herein, is intended to be representative of the duties and responsibilities performed within this classification. It is not necessarily descriptive of any one position in this class. The omission of an essential function does not preclude management from assigning duties not listed herein if such functions are a logical assignment to the position.

• Provides strategic leadership for the organization's AI, machine learning, and advanced analytics capabilities and establishes the vision, roadmap, standards, and operating model for AI-driven innovation
• Directs the design, development, deployment, and lifecycle management of AI solutions, machine learning models, intelligent agents, and advanced analytics applications that address complex business challenges
• Oversees the development of generative AI capabilities, including Retrieval-Augmented Generation (RAG), foundation model customization, vector search technologies, embeddings, and enterprise knowledge integration
• Establishes enterprise standards for agent orchestration, model evaluation, MLOps, monitoring, governance, and responsible AI practices that ensure scalable and reliable solutions
• Leads the application of advanced statistical methods, predictive modeling, experimentation, forecasting, and analytical techniques that support strategic decision-making and operational improvement
• Partners with business, product, and technology leaders and aligns AI/ML priorities, capabilities, and delivery roadmaps with organizational objectives and measurable business outcomes
• Directs the development of AI-ready data assets, feature engineering capabilities, and model training frameworks that support machine learning and generative AI initiatives
• Establishes and enforces governance, security, compliance, documentation, and quality standards that promote transparency, reproducibility, and responsible AI adoption
• Drives performance management and optimization activities across AI models, platforms, and operational processes to maximize accuracy, efficiency, business value, and cost effectiveness
• Manages AI/ML infrastructure, cloud services, vendor relationships, budgets, and resource planning to ensure secure, scalable, and efficient operations
• Builds, leads, and develops a high-performing team of AI/ML engineers, data scientists, and analytics professionals through coaching, mentoring, talent development, and performance management
• Evaluates emerging technologies, industry trends, and market developments and advances the organization's AI, machine learning, and advanced analytics capabilities.

To perform this job successfully, an individual must be able to perform each essential duty and responsibility satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required.

EDUCATION AND/OR EXPERIENCE:

Bachelor’s degree (preferred Masters of Science) in Computer Science, Analytics, or related field from an accredited college or university. Ten (10) years of progressive experience in data engineering, data science, or a related role, with hands-on experience building and deploying machine learning models, including three (3) or more years in a technical leadership or management capacity leading AI/ML teams and delivering enterprise-scale initiatives.

CERTIFICATES, LICENSES, REGISTRATIONS AND DESIGNATIONS:
None

KNOWLEDGE, SKILLS AND ABILITIES

• Advanced knowledge in Python, SQL, machine learning frameworks, and modern analytics technologies used for model development, deployment, and optimization
• Advanced knowledge of generative AI technologies, including AI agents, multi-agent systems, prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation architectures
• Advanced knowledge in supervised and unsupervised machine learning techniques, model evaluation, feature engineering, and performance measurement methodologies
• Proficient in cloud-based AI and analytics platforms, data architectures, data warehousing, and ETL/ELT processes
• Strong knowledge of model customization approaches, including supervised fine-tuning, retrieval-augmented fine-tuning, and parameter-efficient training methods
• Proficient in MLOps, model monitoring, lifecycle management, observability, governance, and responsible AI practices
• Proficient in establishing engineering best practices, including version control, code reviews, testing, CI/CD, and collaborative development standards
• Advanced statistical knowledge in hypothesis testing, experimental design, forecasting, causal inference, regression analysis, and uncertainty quantification
• Demonstrates strong leadership, strategic planning, organizational development, and change management capabilities
• Ability to communicate, engage stakeholders, and demonstrate executive presentation skills and translate complex technical concepts into business value
• Ability to lead enterprise AI initiatives, managing budgets and vendors, and delivering measurable business outcomes through AI, machine learning, and advanced analytics solutions.

PHYSICAL REQUIREMENTS:

The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job.  Functions involve the ability to exert light physical effort usually involving some lifting, carrying, pushing and/or pulling of objects and materials of light weight (up to 20 pounds).  May involve some climbing, balancing, stooping, kneeling, crouching, crawling, walking or standing.  

ENVIRONMENTAL REQUIREMENTS:

The work environment characteristics described here are representative of those an employee may encounter while performing the essential functions of this job.  

Functions are regularly performed inside without potential for exposure to adverse conditions, such as inclement weather, atmospheric elements and pathogenic substances.  The noise level in the work environment is usually moderate.