Manager, Digital Strategy

Annual Wage Range: $115,360.00  - $158,620.00 
Other Compensation:  Eligibility for the Short-Term Incentive program and other applicable bonuses 
Benefits: U.S. Employee Benefits Summary (grace.com)

Grace, a Standard Industries company, is a leading global supplier of catalysts, engineered materials and fine chemicals. We provide innovative products, technologies and services which our customers use to manufacture everyday products like renewable fuels, pharmaceuticals, toothpaste, cosmetics, food packaging, beer, edible oils and more. Our thousands of employees help shape a better future at our global headquarters in Columbia, MD and locations worldwide.

Job Description

Grace is seeking a Digital Strategy Manager to join our IT team! This role reports to VP, Data, AI and Digital organization located at our headquarters in Columbia, MD. 

The Digital Strategy Manager partners with business and technology leaders to shape, prioritize, and govern a portfolio of digital, data, and AI initiatives. This role combines rigorous analysis, process and best practice creation, and strong stakeholder facilitation/collaboration with a specialized focus on AI strategy, data strategy, initiative valuation, and operating model evolution.

You will help the new Grace Data, AI, and Digital organization create the initial enterprise-wide vision and strategy for these areas in close collaboration with Grace business and functional stakeholders.

Responsibilities

• Digital / AI Initiative Cataloging & Valuation
  • Build and create a process for maintaining a comprehensive inventory of digital, data, analytics, and AI initiatives across Grace
  • Align digital / AI intake and assessment frameworks with existing Grace IT operating model, ensuring business cases and value measurement approaches (financial and non-financial), fit with digital / AI initiatives
  • Produce executive-ready dashboards and summaries to assist in management of digital portfolio (demand, value/health, costs, etc)
• Development of 3-Year AI Strategy & Roadmap
  • Facilitate creation of 3‑year AI strategy aligned to business objectives / operational priorities
  • Identify and document AI use cases to establish portfolio and prioritization processes
  • Translate strategy into a clear roadmap: initiatives, milestones, dependencies, required platforms/data investments, and change management needs
  • Generate executive communications on strategy, timing, key decision points, and benefits
• Enterprise Data Strategy & Data Catalog
  • Co-create an enterprise data strategy including principles / best practices, target-state capabilities, and prioritized roadmap for foundational data investments
  • Establish or enhance data governance mechanisms: data ownership, stewardship, quality standards, definitions, etc
  • Support the selection, configuration, and rollout of a data catalog
• Alignment of Data & Analytics Supply / Demand to IT Operating Model & Business Strategies
  • Assess current operating model (including intake-to-delivery processes) for data/analytics/AI work and identify improvements (e.g. roles and responsibilities, data domain ownership/governance, prioritization processes)
  • Evaluation and selection of appropriate delivery organization model (e.g. hub-and-spoke)

Required Qualifications

• Bachelor’s degree in Business, Information Systems, Data/Analytics, Engineering, or related field
• 7-10 years of experience as a Business Analyst, Strategy Analyst, or similar role
• Demonstrated experience with strategic thinking, structured problem solving / hypothesis-driven analysis and translating findings into visions / strategies
• Demonstrated experience building roadmaps, supporting portfolio governance, and developing business cases to support execution of digital visions / strategies
• Experience working with data, analytics, or AI initiatives, especially use case intake, data readiness, governance, and value tracking
• Strong literacy of current / emerging data and AI landscapes / tools
• Modern data platforms and architectures (Snowflake, Databricks, Google BigQuery, etc.)
• Data governance, catalog, and metadata (Purview, Alation, etc.)
• Self-service / BI Analytics tools (Power BI, SAP Analytics Cloud, etc.)
• AI / ML / advanced analytics approaches and tools (CRISP-DM, ML / Data Science governance, etc.)
• Generative AI / Agentic AI (models, orchestration / architectures, responsible AI, etc.)
• Experie