Vice President, Reporting, Analytics & Insights

Tenerity•,

About The Position

Tenerity is seeking a strategic and result oriented Vice President, Reporting, Analytics, & Insights to lead a global team of approximately 20 professionals across reporting, analytics, data science, AI, and machine learning. This leader will serve as a key business partner across operations, products, finance, technology, and client facing teams. The role is responsible for transforming data into actionable insights that improve decision-making, operational performance, customer outcomes, and business value. The ideal candidate combines strong business acumen with analytics leadership experience and can operate effectively at both strategic and execution levels. This role will drive the evolution of the analytics organization from reactive reporting to proactive, insight driven decision support.

Requirements

10 or more years of progressive leadership experience in reporting, analytics, business intelligence, data science, or related disciplines.
Experience leading teams across reporting, analytics, data science, AI, and/or machine learning.
Proven ability to partner with senior business leaders and translate business needs into data-driven solutions.
Strong understanding of BI platforms, data visualization, data warehousing concepts, analytics tools, and modern data environments.
Experience developing and deploying advanced analytics, predictive modeling, AI, or ML capabilities in a business environment.
Demonstrated ability to lead teams through change, improve operating models, and build scalable processes.
Strong business acumen with the ability to connect analytics work to revenue, cost, customer experience, risk, productivity, or operational outcomes.
Excellent communication skills, including the ability to explain complex data concepts to non-technical stakeholders.
Experience establishing governance around metrics, reporting standards, data quality, prioritization, and analytics delivery.

Nice To Haves

Experience in subscription, loyalty, customer engagement, marketing services, or contact center-oriented businesses.
Experience with customer analytics, operational analytics, marketing analytics, client reporting, fraud analytics, or contact center analytics.
Familiarity with tools such as AWS Quicksight, Cognos, Looker, SQL, Python, R, cloud data platforms, and AI/ML platforms.
Experience supporting global business operations or distributed teams.
Experience building analytics capabilities in organizations undergoing transformation or modernization.

Responsibilities

Lead the Reporting and Analytics organization, including reporting, business intelligence, analytics, data science, AI, and machine learning capabilities.
Develop and execute a data and analytics strategy aligned with Tenerity’s business priorities, growth objectives, operational needs, and client commitments.
Establish a clear vision for how data, analytics, AI, and automation can improve business performance, customer experience, operational efficiency, and decision-making.
Create a roadmap that balances foundational reporting needs with advanced analytics, predictive modeling, AI-enabled insights, and scalable self-service capabilities.
Ensure analytics work is connected to measurable business outcomes, not just data delivery or report production.
Serve as a senior liaison between the Reporting and Analytics team and business stakeholders across the organization.
Partner with executive and operational leaders to understand business goals, pain points, performance drivers, and decision-making needs.
Translate business questions into analytical approaches, reporting solutions, data science models, and actionable recommendations.
Help the business move from reactive reporting to proactive insight generation, forecasting, trend identification, and opportunity discovery.
Build strong relationships with stakeholders and establish credibility as a trusted advisor who can connect data to business action.
Oversee the delivery of accurate, timely, and meaningful reporting across operational, financial, client, product, and executive use cases.
Improve reporting consistency, data quality, metric definitions, governance, and executive visibility into key performance indicators.
Drive the evolution of reporting from static dashboards to more dynamic, self-service, and insight-oriented analytics capabilities.
Ensure reporting solutions provide clear visibility into performance, trends, risks, opportunities, and business outcomes.
Partner with technology and data teams to improve the underlying data environment, reporting architecture, and BI tools.
Lead the development and application of data science, AI, and machine learning capabilities across the business.
Identify practical use cases for predictive analytics, customer segmentation, anomaly detection, operational optimization, automation, and decision support.
Ensure AI and ML initiatives are aligned with real business needs and have clear success measures.
Create a disciplined approach to model development, testing, deployment, monitoring, and governance.
Partner with technology, product, operations, and compliance teams to ensure AI solutions are scalable, ethical, explainable, and operationally sustainable.
Lead, develop, and mentor a team of approximately 20 professionals across reporting, analytics, data science, and related functions.
Build a high-performing team culture focused on business partnership, accountability, quality, curiosity, innovation, and measurable outcomes.
Create clear roles, responsibilities, operating rhythms, intake processes, prioritization methods, and delivery expectations.
Develop talent within the organization and ensure the team has the skills needed to support both current reporting needs and future analytics capabilities.
Promote collaboration across analysts, data scientists, business stakeholders, technology teams, and operational leaders.
Establish strong governance around analytics intake, prioritization, delivery, data definitions, metric standards, and stakeholder communication.
Ensure the team focuses on the highest-value work and avoids becoming a reactive report factory.
Implement clear prioritization methods that balance urgent business needs, strategic initiatives, regulatory or client obligations, and long-term capability building.
Measure and communicate the impact of analytics work through business outcomes, adoption, efficiency gains, revenue impact, risk reduction, or improved decision quality.
Ensure projects are delivered with appropriate quality, documentation, stakeholder alignment, and change management.