Sr. Manager, Business Transformation & Analytics

Culligan Quench•King of Prussia, PA

1d•$120,000 - $130,000

About The Position

Culligan Quench’s purpose is to impact people’s lives and improve the earth by helping to eliminate the 500 million plastic bottles consumed each year. We play a front-line role in the battle against single-use plastic water bottles by delivering on-demand filtered water solutions to more than 120,000 healthy and environmentally conscious customers across North America. Our bottle-free water coolers, ice machines, sparkling water dispensers and coffee brewers purify the existing water supply, providing an endless supply of clean water and water-based beverages for a fixed monthly fee, typically under a long term bundled service and rental subscription agreement. Culligan Quench has grown from a small regional company to an international leader that had a successful NYSE public offering in 2016 and is now a strategic company owned by private equity backed Culligan. Headquartered in King of Prussia, PA, Quench has more than 1,600 team members operating out of more than 90 locations across North America and Puerto Rico. For more information visit https://quench.culligan.com/ About Culligan There’s nothing more fundamental to life on Earth than water. At Culligan, we believe transforming water can transform the world. That's why we offer expert water services and industry-leading solutions globally. Local Culligan teams deliver better drinking water to people at work, at home and on the go. With expertise across service, science and sustainability, Culligan turns water you can live with into water you can love. For more information visit www.culligan.com . Values: 5Cs Culligan as One Customers come first Commitment to Innovation Courage to do what's right Consistently deliver exceptional results Culligan Quench seeks a Sr. Manager, Business Transformation & Analytics to lead a mission-critical mandate: deploy and operate AI-powered automation across Customer Care & Field Service, create operating leverage at scale, and build analytics capabilities that make performance visible and actionable. The Sr. Manager is a builder, operator, and improver —not an evaluator. This leader owns the full lifecycle of AI-powered automation across Customer Care & Field Service: simplify processes, deploy AI and automation, run the synthetic workforce as an operating capacity layer, and define the KPIs that prove progress. The role is accountable for three outcomes: (1) Improve customer experience and tNPS by making interactions faster, cleaner, and more consistent—and reducing time to resolution. (2) Generate operating leverage by scaling capacity without proportional headcount growth. (3) Modernize analytics by moving from Excel-based reporting to self-serve Power BI and linking operational metrics to P&L impact. The role directly leads the Analytics function (one internal analyst and one external analytics partner). This team provides the data infrastructure, reporting, and insights that translate operational activity into business clarity. This role reports to the SVP, Customer Care & Field Service.

Requirements

Strong financial modeling skills; comfortable building ROI cases, establishing baselines, and presenting to executive audiences.
Excellent communicator; translates technical complexity into plain operational language and operational needs into requirements for technology partners.
Experience managing a small analytics team or individual contributors, including vendor/partner relationships; able to develop talent and direct analytical priorities.
Exposure to Lean, Six Sigma, or structured CI methodology—able to partner with a CI function without duplicating it.
Ability to manage cross-functional implementation projects (IT, operations, vendor partners) without direct authority over all stakeholders.
Operator first: measures success in customer outcomes and cost per transaction, not tool deployment milestones or innovation theater.
Owns the production environment: accountable for what's live, not just what was launched.
Process before automation; simplifies before automating and never automates a broken process.
Skeptically optimistic: rigorous about AI claims; equally willing to kill a pilot and scale a winner.
Influence without authority earns trust with frontline supervisors, IT partners, and finance stakeholders.
Transparent escalator: surfaces risks and bad news early; doesn't manage up through spin.
Resilient and high-energy; comfortable driving to answers cross-functionally.
Comfortable navigating ambiguity and evaluating emerging technologies responsibly.
Effective time manager who prioritizes based on value.
High integrity; ownership-oriented and learning-focused when addressing mistakes.

Responsibilities

Process Automation and Transformation Identify and eliminate manual, rules-based work suitable for automation (e.g., work order creation, ETAs, scheduling confirmations, status updates, data entry, order-processing triggers). Set baseline cycle-time benchmarks before deployment and track post-launch improvements. Own the automation roadmap; prioritize by effort-to-impact and maintain leadership visibility into the pipeline.
AI Solution Implementation Lead end-to-end implementation of AI solutions: scope, pilot, deploy, stabilize, and scale—with clear go/no-go criteria at each gate. Own live deployments and the production environment—not just the launch. Partner with implementation teams, IT, and business SMEs on integrations; resolve technical and operational blockers with appropriate escalation. Ensure each solution has a rollback plan, escalation path, and human-in-the-loop override. Maintain an inventory of AI-assisted touchpoints across the customer journey and track uptime, accuracy, and containment.
Management of the Synthetic Workforce Own and operate the synthetic workforce as a capacity layer (AI agents, automated workflows, bot-handled contacts) with clear performance standards. Define performance targets (containment, accuracy, cycle time, error rate, escalation rate) and hold the synthetic workforce to the same accountability as human teams. Run regular workforce reviews: identify underperforming agents, retrain/reconfigure models, and retire what doesn’t deliver. Manage handoffs between synthetic and human agents so escalations preserve context and avoid restarting the experience. Report synthetic workforce performance alongside human workforce metrics; make synthetic capacity visible in staffing models.
Ownership of Analytics Directly manage one internal analyst and one external analytics partner; set clear expectations, prioritize work, and develop both resources. Ensure timely, accurate KPI reporting in support of Culligan’s Business System (Bowlers, Root Cause/Countermeasure, etc.). Own the reporting infrastructure that supports the AI and synthetic workforce program. Direct the internal analyst toward high-value analytical work (root cause analysis, trend identification, performance modeling); protect their time from low-leverage reporting tasks. Manage the external partner: define scope, enforce quality and timelines, assess value, and own renew/replace decisions. Post-implementation, define the KPIs that reflect what changed for customers and the business—don’t inherit generic metrics. Build dashboards that make AI and automation performance clear to frontline managers, VPs, and the SVP. Translate operational metrics into P&L impact so productivity gains show up in the operating model—not just dashboards.