Senior Manager Analytics

Caterpillar Inc.•Peoria, IL

2d•Onsite

About The Position

This position is responsible for managing the development, implementation and support of data, analytics, and AI enablement across enterprise compliance program initiatives and data platforms. Provides leadership to ensure data and analytics capabilities are scalable, reusable, and aligned to goals across the enterprise compliance program.

Requirements

Analytical Thinking: Designs and orchestrates the use of analytics and AI for strategic decision‑making. Champions advanced analytics, predictive insights, and prescriptive decision support. Enables leaders to use analytics insights effectively, not just consume dashboards. Implements outcome‑focused operating metrics and performance measures. Applies quantitative, statistical, and modeling approaches to complex business problems. Chooses among a diverse set of analytical tools according to the nature of the situation.
Business Statistics: Generates and interprets a wide range of statistical data and reports. Utilizes in-depth knowledge of several statistical tools or applications. Consults and coaches others on sampling approaches and associated statistical significance.
Innovation: Finds new or non-traditional ideas to improve effectiveness in team's area of responsibility. Guides team members in the development and fulfillment of proposed innovations. Fosters a team culture that encourages exploration of non-traditional ideas. Implements strategies for renewing or deepening change efforts.
Continuous Transformation: Leads adoption of new platforms, tools, and processes as business and technology evolve. Adapts operating models to support analytics‑driven and AI‑enabled ways of working. Encourages experimentation, learning, and continuous improvement. Communicates lessons learned from both successes and failures.
Talent Management: Builds and sustains a strong pipeline of data, analytics, and AI leadership talent. Coaches leaders and teams to grow strategic, not purely technical, capabilities. Establishes clear role expectations and development paths aligned to future needs. Creates an environment that attracts and retains high‑impact data and analytics talent.
Leadership: Inspires teams with a clear vision for analytics‑led and AI‑enabled decision making. Coaches leaders in adaptive leadership approaches appropriate to complex, matrixed environments. Models behaviors that promote accountability, trust, and collaboration. Drives alignment around long‑term data and AI strategy.
Matrix Management: Communicates effectively across business, technology, and data domains. Balances functional priorities with enterprise‑level outcomes. Resolves competing requirements across solutions, platforms, and stakeholder groups. Maintains strong partnerships across geographic and organizational boundaries.
Machine Learning: Completes specific tasks and initiatives utilizing machine learning technologies, such as search engine optimization. Utilizes specific tools and techniques to process descriptive and inferential statistics. Applies specific computing languages and tools in machine learning, such as R and Python. Explores to use machine learning to make improvements. Conducts data mining and cleaning initiatives.

Responsibilities

Managing the development, implementation and support of data, analytics, and AI enablement across enterprise compliance program initiatives and data platforms.
Providing leadership to ensure data and analytics capabilities are scalable, reusable, and aligned to goals across the enterprise compliance program.
Identifying, sizing and prioritizing opportunities for improvement through analytics.
Assessing the scope and resources required of various analytics projects and working with manager to define necessary resources.
Managing client relations to ensure that project expectations are managed, and fulfilled.
Monitoring and communicating key performance metrics and operational challenges.
Provide strategic direction to ensure data, analytics, and AI capabilities meet compliance program needs, decision requirements, and future scalability.
Establish and enforce standards for data quality, lineage, governance, and reuse across the program.
Maximize staff contribution through coaching, professional growth, and capability development.
Ensure analytics and AI deliver actionable insights, not just reports or models.