Senior Manager - Data Analytics

Caterpillar•Mossville, IL

1d•Onsite

About The Position

The Senior Manager – Connectivity Data Analytics & AI Transformation is accountable for driving data‑ and AI‑enabled engineering transformation across Caterpillar’s Connectivity organization , with Cat® Helios as the foundational data and analytics platform. This role leads the strategy, delivery, and adoption of analytics, automation, and AI solutions that improve connectivity quality, engineering efficiency, system reliability, and customer outcomes across the end‑to‑end connectivity ecosystem (on‑machine, off‑machine, dealers, and enterprise consumers). The position combines people leadership , technical judgment , and enterprise change leadership , translating connectivity data into trusted insights and scalable AI capabilities that transform how engineering and service organizations operate.

Requirements

Bachelor’s degree in Engineering, Computer Science, Data/Analytics, or related quantitative field (or equivalent experience)
Strong experience leading analytics and/or AI initiatives in complex engineering or technology environments
Demonstrated people leadership experience managing managers and technical professionals
Proven ability to translate ambiguous engineering problems into scalable data and AI solutions

Nice To Haves

Master’s degree (Engineering, Data Science, Computer Science, or MBA)
Experience with connected systems, telematics, IoT data, or embedded/on‑machine plus cloud ecosystems
Hands‑on familiarity with modern data platforms, analytics tooling, and ML/AI delivery patterns
Experience operating in a governed enterprise AI environment
Advanced analytical thinking and problem structuring
Connectivity, telemetry, or large‑scale operational data understanding
Data modeling, KPI design, and analytics product ownership
Working knowledge of ML/AI and GenAI application patterns
Understanding of MLOps/LLMOps concepts (monitoring, drift, retraining)
Enterprise transformation and change leadership
Executive communication and data storytelling
Cross‑functional influence without direct authority
Strong judgment balancing speed, rigor, and risk

Responsibilities

Define and own the Connectivity Analytics & AI roadmap, aligned to Connectivity strategy, Digital Data & AI standards, and Helios platform capabilities
Identify, size, and prioritize high‑value use cases (e.g., data quality, validation automation, anomaly detection, predictive reliability, AI‑assisted diagnostics, engineering productivity enablement)
Translate connectivity engineering problems into analytics and AI initiatives with clear value metrics (cost, quality, speed, risk reduction)
Lead development and governance of connectivity data products built on Helios (semantic models, curated datasets, APIs, dashboards)
Partner with Helios platform teams, data engineering, and governance functions to ensure scalability, compliance, and reuse
Drive proactive data quality rule definition, monitoring, and remediation across connectivity data pipelines
Apply AI/ML and GenAI capabilities to connectivity engineering workflows, including: Validation and test automation, Fault, anomaly, and pattern detection, Predictive insights for reliability and service readiness, AI‑assisted documentation, diagnostics, and root‑cause analysis
Ensure AI solutions progress from pilot → production → sustained adoption
Establish standards for responsible AI, model evaluation, monitoring, retraining, access control, and audit readiness
Manage a portfolio of analytics and AI initiatives across Connectivity Quality, Validation, Engineering Efficiency, and Service Solutions
Ensure disciplined execution: scope definition, resourcing, dependency management, risk mitigation, and delivery governance
Provide executive‑ready insights and narratives that translate data and AI outputs into decisions and actions
Act as a trusted analytics and AI advisor to Connectivity leadership
Partner closely with: Connectivity Engineering & Validation, Cat Digital / Helios platform teams, Service Engineering & Dealer Support, Cyber, Compliance, and Data Governance
Lead adoption, enablement, and change‑management efforts to embed analytics and AI into daily engineering and operational workflows
Build and lead a high‑performing team spanning analytics, data engineering, analytics engineering, and applied AI
Develop talent, clarify roles, and establish standards for technical excellence, delivery quality, and continuous improvement
Foster a culture that values data‑driven decisions, automation, reusability, and measurable outcomes