Safety Excellence Data & Systems Analyst
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Job Summary
About the role
We have a new opportunity for an experienced data and systems professional to join our team as Data & Systems Analyst. This is a critical role within our multi-year Safety Excellence programmea cultural transformation initiative focused on elevating how we work safely every day at every level.
As a Data & Systems Analyst you will create a standardised approach to collecting and analysing safety information across our manufacturing sites. You will transform raw data into meaningful insights to support risk reduction and strategic decision-making. A key focus will be evolving our digital landscape evaluating our current system identifying functionality gaps and supporting the implementation of a future-fit management platform.
Key responsibilities include:
- Data Analysis & Refinement: Extract and polish data from current systems converting it into meaningful analysis to identify trends risks and opportunities
- System Evolution: Evaluate current systems to identify gaps and infrastructure needs; lead the identification and implementation of a future management system
- Dashboards & Reporting: Design and maintain automated tracking reports and visual dashboards in Power BI for both Safety Excellence and Learning data
- Systems Support: Ensure smooth functionality of digital tools and learning platforms validating new content and managing integrations between business systems
- Stakeholder Collaboration: Partner with programme leads and site teams to share insights resolve data issues and ensure actions are followed through
- Continuous Improvement: Proactively recommend ways to improve data processes system usability and the sharing of best practices across regions
About you
- Demonstrable experience in managing large datasets and using Power BI to create high-impact visual reporting and performance tracking
- Proven ability to support or implement digital information systems; knowledge of MS Fabric or safety management software is highly desirable
- Proficient with AI tools and curious about leveraging new technologies to improve safety and learning outcomes
- Background in data analysis within a manufacturing or multi-site environment ideally focused on safety or operational data
- Ability to translate complex data into clear insights for stakeholders at all levels with strong problem-solving skills and attention to detail
Location: This is a remote role with approx. 20% travel within EMEA.
About us
We are DS Smith together with International Paper we are a global leader in sustainable packaging solutions and other fibre-based believe a better more sustainable tomorrow is possible with the right people who challenge and support one another to enact positive change. We employ more than 65000 colleagues in North America and Europe Middle East and Africa (EMEA) who are experts in innovationmanufacturing design sales sustainability supply chain and much more. Together with our customers we make the world safer and more productive one sustainable packaging solution at a time. Become part of a world-leading organisation and do your best work with us!
Fair recruitment at DS Smith
To fulfil our purpose of redefining packaging for a changing world we aim to build a diverse motivated and engaged workforce. Our goal is to create a culture of inclusion where everyone is treated fairly differences are valued and everyone has an equal opportunity to succeed.
Our people come from diverse backgrounds bring different perspectives ideas and experiences to generate unique solutions focused on present and future sustainability challenges. We welcome all candidates to apply even those not meeting all criteria.
#LI-NS
Required Experience:
IC
Key Skills
- Data Analytics
- Microsoft Access
- SQL
- Power BI
- R
- Data Visualization
- Tableau
- Data Management
- Data Mining
- SAS
- Data Analysis Skills
- Analytics
About Company
DS Smith is an international packaging company, offering sustainable, plastic-free packaging, integrated recycling services, and sustainable paper products.
