In this role, a typical day will include:
Enterprise Data Science Strategy for Manufacturing
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Manage a 2–3-year capability roadmap aligned to manufacturing priorities
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Define a portfolio of scalable use cases with clear value hypotheses, ROI models, and adoption plans
Rapid Pilots → Scaled Deployment
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Stand up lean pilots in weeks, not months, to gain quick insight into the rapidly evolving AI/ML and visualization landscape
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Codify pilots into repeatable playbooks and templates; drive scale‑up to all eligible sites
Global KPI Ownership & Evolution
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Own business definition, data lineage, and governance for global dashboards (e.g., PPV, inventory, OEE)
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Extend dashboards with advanced capabilities including forecasting, anomaly detection, scenario simulation, and automated alerts
Partner Ecosystem & Vendor Strategy
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Identify and manage 3rd‑party providers to complement internal teams to speed up time‑to‑value.
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Structure outcome‑based SOWs, price-to-value models, and success criteria.
Manufacturing Data Products
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Influence data architectures to create appropriate capabilities and scale that meet manufacturing’s requirements.
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Ensure operational AI/ML has appropriate controls in place to manage quality and business risks
Change Management & Adoption
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Build solutions that scale natively by deeply understanding use-cases and partnering with global and local teams for tech stack standardization
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Build site champion networks, training, and hiring/upskilling plans to drive sustained use of solutions and talent pipeline
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Build and maintain a ranked use‑case portfolio with quantified value, complexity, time‑to‑impact.
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Align with Finance/Procurement/Operations on PPV, inventory, yield value levers and measurement.
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Publish a quarterly “State of Data Science in Manufacturing” progress and impact report.
Solution Lifecycle Leadership
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Lead ideation → prioritization → pilot → implementation → scale of use cases.
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Define acceptance criteria and guardrails for site rollout (data readiness, process readiness, training)
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Own global business requirements and success metrics; partner with IT, vendors, and sites for technical delivery
KPI & Analytics Product Ownership
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Serve as the Product Owner for global KPI dashboards
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Establish KPI definitions, visualization guidelines, and drill‑down standards
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Implement advanced KPIs capabilities like predictive, scenario, and alerting concepts
Stakeholder & Vendor Management
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Build trust with Site Leaders, Supply Chain, Procurement, Quality, Finance, and IT/Data teams
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Select and manage vendors; negotiate deliverables, SLAs, and commercial terms tied to business impact
Operations & MLOps Oversight
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Ensure models/applications have clear owners, monitoring plans, and retraining/optimization procedures
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Partner with global and site IT, quality, compliance teams to ensure compliant, secure deployments
Culture, Capability, and Ways of Working
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Champion a product mindset, reproducibility, and documentation standards
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Develop playbooks for common patterns (e.g., demand sensing, inventory optimization, process anomaly detection, computer vision QC)
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Mentor site and functional teams on data literacy