VP, Data, Analytics, & AI

Ferrara•Chicago, IL

2d•$192,416 - $269,382•Onsite

About The Position

Reporting directly to the CIO, the VP, Data, Analytics & AI will partner with our business leaders in building our future. Our ambitious goals for expansion are driving us to transform our core business processes and build a technical landscape that improves the effectiveness of our company. Successful candidates will need to demonstrate prior leadership in driving positive change and having a seat at the table of the functional leadership team as well as the IT Leadership team. Previous positions leading Analytics and Data will be considered an advantage The VP, Data, Analytics & AI is an executive leadership role focused on leveraging data, analytics, and AI to drive business value, foster a data-driven culture, and ensure the effective governance and utilization of data assets across the organization. This includes the creation and management of data and analytics strategy and operating model. The VP, Data, Analytics & AI is responsible for establishing, leading, and operating the data and analytics (D&A) function; building trust and managing data; evolving technology capabilities; and developing talent and D&A culture.

Requirements

Technical Skills: Expertise in data management, information/data architectures, data structures, data and analytics governance, statistical skills, and knowledge of information systems/tools.
Strategic thinking and planning
Management of Capital Investments and Expenses
Deep knowledge of data, analytics and AI capabilities and evolving technologies
Leadership skills in directing staff to achieve objectives
Mentorship, Coaching and Career Development
Persuasive communication and advocacy
Strong interpersonal, written, and oral communication skills
Discuss technical topics in a user-friendly language
Highly self-motivated and directed
Education: Preferably a bachelor’s or master’s degree in business administration, STEM, computer science, data science, information systems, or related field. Academic qualification or professional training in legal and regulatory areas is desirable.
Experience: 10 to 15 years of business experience, ideally in business management, legal, financial, or IT management. Five or more years of progressive leadership experience in leading cross-functional teams and enterprisewide data and analytics programs.
Stakeholder Management: Identify and prioritize internal and external stakeholders and their goals. Connect with stakeholders to identify unmet needs and redesign business processes or address data-related risks.
KPIs and Business Outcomes: Inventory key performance indicators (KPIs) and use them to improve business outcomes through specific actions. Create a value-based story of how data and analytics capabilities are necessary for a successful organization.
Data and Analytics Expertise: Broad understanding of strategic data and analytics capabilities and the ability to communicate these concepts to stakeholders. Support AI initiatives and data-driven culture change.
Management and Operations: Develop, manage, allocate, and govern the annual budget for the D&A function. Organize and lead a data and analytics center of excellence. Oversee the development and maintenance of the organization’s data and analytics architecture and platforms.

Responsibilities

Strategy and Vision: Define the data, analytics, and AI strategy, including vision, drivers, and outcomes. Lead the creation and ensure the ongoing relevance of the organization’s D&A strategy in collaboration with the CEO, business domain leaders, CIO, and other relevant stakeholders.
Operating Model: Institute an operating model for data, analytics, and AI that aligns with the capabilities and competencies required to execute the strategy. This includes the ecosystem, architectures, and delivery model.
Partnerships: Build partnerships with executive leadership and board members to ensure data is managed as a business asset, AI-ready, and track and measure the value derived from those data assets. Communicate the tangible business value generated from data, analytics, and AI initiatives to stakeholders and executives.
Data Governance: Maintain authority and accountability for data assets, analytics used for decision-making, and AI solutions that automate decisions and augment human performance. Oversee a centralized data management/data engineering service to ensure quality, traceability, timeliness, usability, and cost-effectiveness.
Delivery Models: Oversee delivery models, methods, and practices for creating data, analytics, and AI products to ensure consistent application and use of data and analytics solutions and services, including data science.
Technology Capabilities: Evolve technology capabilities for the D&A platform in collaboration with the CIO to align D&A initiatives with IT infrastructure and policies and drive technology innovation across the organization.
Governance Mechanisms: Establish and maintain trust in AI-ready data assets by instituting governance mechanisms for data, including fostering data stewardship across business data domains. Collaborate with leaders responsible for security, privacy, risk, and compliance.
Regulatory Compliance: Understand regulatory requirements, relevant data protection laws and regulations, and industry-specific standards. Ensure the organization's data practices are compliant in collaboration with legal and compliance teams.
Ethical Use of Data: Oversee the ethical and responsible use of data and algorithms for AI, analytics, and automated decision-making. Establish guidelines and practices to prevent misuse of data and protect individual privacy.
Culture and Talent Development: Own the development of a data-driven culture, related competencies, behaviors, and data and AI literacy across the enterprise. Lead transformation efforts by developing D&A talent and maturing the D&A capability of the organization.
Innovation: Lead data-driven innovation for the enterprise, including the investigation, adoption, and exploitation of AI. Identify new data sources to enable business value innovation and new use cases. Monitor emerging skills and technologies to accelerate business innovation and transformation.
Product Development: Lead research, strategy creation, and development of new data and analytics products or services to expand markets, monetize data, and impact enterprise profitability.
Public Sector (Optional): Create and expand open data public offerings to empower citizens, advance transparency, and enable better government and commercial services.