Director, Advanced Manufacturing Analytics

Alcon•Fort Worth, TX

18h

About The Position

At Alcon, we're passionate about enhancing sight and helping people see brilliantly. With more than 25,000 associates, we innovate fearlessly, champion progress, and act swiftly to impact global eye health. We foster an inclusive culture, recognizing your contributions and offering opportunities to grow your career like never before. Together, we make a difference in the lives of our patients and customers. Are you ready to join us? We foster an inclusive culture and are looking for diverse, talented people to join Alcon. Alcon is accelerating its digital manufacturing ambitions, integrating modern operational technologies with enterprise digital systems to enable world‑class performance across its 16+ global manufacturing sites and across the full supply chain. Central to this transformation is the strategic development and deployment of insights and functionality on top the digital landscape. 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:

Requirements

Bachelor’s Degree or Equivalent years of directly related experience (or high school +18 yrs; Assoc.+14 yrs; M.S.+7 yrs; PhD+3 yrs)
The ability to fluently read, write, understand, and communicate in English
10 Years of Relevant Experience

Nice To Haves

Background in process manufacturing or discrete multi‑site operations; exposure to OEE, yield, scrap, PPV, inventory, S&OP.
Knowledge of industrial data (MES/SCADA/Historians/IoT), time‑series modeling, and computer vision for quality inspection.
Experience in regulated environments (e.g., GxP, CSV) and audit‑ready analytics.
MBA, MS/PhD in a quantitative field, or equivalent experience blending business + analytics.
Leadership Competencies Influence Without Authority: Aligns diverse stakeholders; secures resources and decisions across functions and sites.
Results Mindset: Prioritizes outcomes over output; maintains clear roadmaps and success metrics.
Bias for Action: Moves from concept to pilot quickly; iterates based on evidence.
Strategic Thinking & Systems View: Connects network‑level levers (cost/service/quality) to local actions.
Communication: Translates technical detail into executive-ready narratives and operator‑level guidance.
Vendor & Partner Stewardship: Builds durable, value‑based partnerships; holds vendors accountable to impact.
Talent Development: Improves data literacy across operations; mentors analysts and engineers (direct or dotted line) fostering inclusive debate, diverse perspectives, and fail-fast learning.

Responsibilities

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 Champion a product mindset, reproducibility, and documentation standards Develop playbooks for common patterns (e.g., demand sensing, inventory optimization, process anomaly detection, computer vision QC) Mentor site and functional teams on data literacy