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Director, Advanced Manufacturing Analytics
Job in
Fort Worth, Tarrant County, Texas, 76102, USA
Listed on 2026-06-05
Listing for:
Alcon MX
Full Time
position Listed on 2026-06-05
Job specializations:
-
IT/Tech
Data Analyst, Data Science Manager
Job Description & How to Apply Below
The Director of Advanced Manufacturing Analytics will own the strategic development and deployment of advanced data analytics capabilities across a multi‐site manufacturing and supply chain network. This is a business- and application-focused role that identifies high‐value opportunities, rapidly pilots solutions, and scales proven capabilities to all sites by orchestrating internal data science and data engineering teams, site operations, and selected third‐party partners.
You will also serve as the business owner for global KPI dashboards, evolving them into predictive and prescriptive tools for metrics such as Purchase Price Variance (PPV), Inventory, Yield, OEE, Schedule Attainment, and more.
** In this role, a typical day will include:
**** Enterprise Data Science Strategy for Manufacturing
*** Manage a 2–3-year capability roadmap aligned to manufacturing priorities
* Define a portfolio of scalable use cases with clear value hypotheses, ROI models, and adoption plans
** Rapid Pilots → Scaled Deployment
*** Stand up lean pilots in weeks, not months, to gain quick insight into the rapidly evolving AI/ML and visualization landscape
* Codify pilots into repeatable playbooks and templates; drive scale‐up to all eligible sites
** Global KPI Ownership & Evolution
*** Own business definition, data lineage, and governance for global dashboards (e.g., PPV, inventory, OEE)
* Extend dashboards with advanced capabilities including forecasting, anomaly detection, scenario simulation, and automated alerts
** Partner Ecosystem & Vendor Strategy
*** Identify and manage 3rd‐party providers to complement internal teams to speed up time‐to‐value.
* Structure outcome‐based SOWs, price-to-value models, and success criteria.
** Manufacturing Data Products
*** Influence data architectures to create appropriate capabilities and scale that meet manufacturing’s requirements.
* Ensure operational AI/ML has appropriate controls in place to manage quality and business risks
** Change Management & Adoption
*** Build solutions that scale natively by deeply understanding use-cases and partnering with global and local teams for tech stack standardization
* Build site champion networks, training, and hiring/upskilling plans to drive sustained use of solutions and talent pipeline
** Strategy & Portfolio
*** Build and maintain a ranked use‐case portfolio with quantified value, complexity, time‐to‐impact.
* Align with Finance/Procurement/Operations on PPV, inventory, yield value levers and measurement.
* Publish a quarterly “State of Data Science in Manufacturing” progress and impact report.
** Solution Lifecycle Leadership
*** Lead ideation → prioritization → pilot → implementation → scale of use cases.
* Define acceptance criteria and guardrails for site rollout (data readiness, process readiness, training)
* Own global business requirements and success metrics; partner with IT, vendors, and sites for technical delivery
** KPI & Analytics Product Ownership
*** Serve as the Product Owner for global KPI dashboards
* Establish KPI definitions, visualization guidelines, and drill‐down standards
* Implement advanced KPIs capabilities like predictive, scenario, and alerting concepts
** Stakeholder & Vendor Management
*** Build trust with Site Leaders, Supply Chain, Procurement, Quality, Finance, and IT/Data teams
* Select and manage vendors; negotiate deliverables, SLAs, and commercial terms tied to business impact
** Operations & MLOps Oversight
*** Ensure models/applications have clear owners, monitoring plans, and retraining/optimization procedures
* Partner with global and site IT, quality, compliance teams to ensure compliant, secure deployments
** Culture, Capability, and Ways of Working
***…
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