I design solutions that fit the problem, not just the technology. Engineer by training, business thinker by choice, problem solver by habit.
I'm an engineer who thinks in business outcomes, and a business thinker who can implement technically. That combination is what my career has been building toward, and what my Master's formalised.
Over the past eight years I've worked across IT operations, service management, and academic research at organisations like Maersk, NATO, Capgemini, and the LEGO Group. I've run multi-site IT infrastructure, automated incident management at scale, and designed data governance frameworks from scratch. What connects all of it is a consistent approach: understand the full picture before proposing a solution, then build something that actually fits.
I finished a two-year Master's in sixteen months. Not because I was in a hurry, but because I set a high bar and held it. I'm now looking for a role where I can apply that same discipline to real organisational challenges, whether that's in IT service management, digital transformation, process improvement, or project delivery.
Completed a 120 ECTS master's programme designed for 24 months in just 16 — graduating 8 months ahead of schedule. Conducted applied research and industry projects inside the LEGO Group, Maersk, and Port Esbjerg, operating at the intersection of engineering rigour and business strategy.
Three and a half years of software engineering fundamentals — from object-oriented design and system architecture through to databases, agile delivery, and end-to-end product development. Thesis delivered as a live full-stack application for a real client, shipped to production with JWT authentication, five CI/CD pipelines, and real-time booking via SignalR.
Two-part applied research inside the LEGO Group — first auditing cross-system data misalignment across 40,577 packaging files, then designing and operationalising a complete data governance framework with Python automation and 100% anomaly detection in under one minute.
The LEGO Group's Packaging Department tracked materials across four systems — SAP, Impact, CF2, and PDF — that were supposed to be perfectly synchronised. Nobody had formally measured how far reality deviated from that ideal. I analysed all four, quantified the misalignments, and categorised errors by root cause. The problem was not technical but organisational: missing governance roles, informal data habits, no enforced policies.
Built on the audit findings to design and operationalise a complete governance framework — from problem diagnosis to solution and strategy. Key deliverables: extended naming conventions, Python automation, a KPI dashboard, and a staff training programme. Introduced a temporary Data Governance Specialist role to bridge the gap between informal practices and a self-sustaining governed environment.
Business model assessment for drone integration at Europe's leading offshore wind port — modelling As-Is and To-Be states, defining 6 use cases across 4 drone types, and designing a low-CAPEX full-lease strategy with an Infrastructure-as-a-Service revenue model for external operators.
Technological Business Model Innovation
Assessed drone integration for Europe's leading offshore wind port as part of the EU DIOL Programme (Interreg North Sea). Modelled As-Is and To-Be business states using BMC and Cambridge BMIP, defined 6 operational use cases across 4 drone types (inspection, security, logistics, surveying), and designed a full-lease low-CAPEX strategy that avoids ownership risk while enabling an Infrastructure-as-a-Service revenue stream for external operators.
Like-for-like evaluation of AI versus manual processing on 3,950 live ServiceNow tickets from Maersk — identifying a 59.87% AI error rate in CI classification, then designing a hybrid AI-human framework projected to grow AI accuracy from 26% to 50% within 12 months while cutting classification costs by 92%.
AI vs Manual Analysis · Technology Specialisation 1
Processed 3,950 live ServiceNow incident tickets from Maersk through both manual review and AI (Microsoft Copilot Pro) — a structured like-for-like evaluation that identified a 59.87% AI error rate in Configuration Item classification. The study went beyond measurement: it proposed a phased hybrid AI-human framework with Power Automate integration, projecting AI accuracy growth from 26% to 50% over 12 months while halving the cost of manual processing.
Most professionals go deep in one direction — technical or commercial. My education was built around the space between them, and every role I have held has required working fluently in both. I can read a technical architecture and a business case, and I know which one is actually driving the decision.
Messy data, unclear processes, undefined problems — I find the structure inside them. I do not propose solutions until I have quantified what is broken, and I use structured methods to make sure the diagnosis holds up before recommending what to fix.
I do not hand off halfway through. Whether it is a hardware rollout, a governance framework, or software shipped to a live client, I take work from problem definition to working solution. I finished a two-year Master's degree in sixteen months. That is how I approach things.