I own the manufacturing decision loop — detect, measure, explain, intervene, standardize — turning fragmented plant, lab, supplier and ERP data into causal root causes and measured shop-floor change.
I turn fragmented plant, lab, supplier & ERP data into causal root causes, production policies, and measurement gates operators actually use.
Not just models — measured change in scrap, cost and process behaviour, in production. Detect a problem, measure it objectively, explain why it happens, intervene at the right lever, and standardize the fix so it sticks.
My edge is the rare overlap most heavy-industry data work is missing: foundry metallurgy, ERP / SAP master data, and data science in one head — the moat that turns a model into a trusted shop-floor decision. 14+ years across automotive, marine and aerospace casting.
And I've run the floor, not just analysed it — Technical Director / Head of Foundry leading operations, AS9100 certification and capital projects. Two tracks, one engineer: production & operations leadership and manufacturing data science.
Integrated five data systems across ~2,000 production charges. Causal inference + Design of Experiments isolated a supplier-chemistry × furnace-state interaction — driving a zero-CAPEX process-policy change plus a production fix, and replacing subjective inspection with quantitative acceptance criteria.
Modernized the foundry's engineering: introduced a simulation-first casting-design approach (Flow3D) and data-based process development, and streamlined prototyping to sub-5-day 3D-printed-mould iterations. The new method lifted yield +50% and cut chill usage −60%, root-caused magnesium & copper-alloy defects, and qualified the shop for AS9100 aerospace work — incl. design support to Rimac.
Production computer-vision — fine-tuned instance segmentation for automated grain sizing. Offline desktop app with an active-learning correction flywheel; now the objective measurement gate.
An MCP server making auditable analysis the path of least resistance: enforced pre-registration, hash-chained lineage, deterministic outputs. Proven end-to-end on a real foundry root-cause study.
Analytical data cube + relationship graph + automated 10-pillar governance audit + SPC — built to expose cost and process deviations invisible across siloed systems.
~90 models across five defect types with SHAP-driven process-driver science and rigorous feature engineering — scrap moved from after-the-fact reporting to forward forecasting.
A personal "second brain" — an MCP server + desktop app that turns a knowledge vault into an agentic system: semantic search, schedulers, and multi-model orchestration the AI calls to run real workflows end to end.
A standalone app that automates foundry scrap-rate roll-downs and SAP write-back from the unified data cube — replacing manual corrections with a governed, repeatable pipeline.
Foundry Manager (2022–2024), then realigned to the data & analysis expert role. Owns the manufacturing decision loop — the Closed-Loop Quality Intelligence program (root-caused a ~CHF 5M scrap defect open 5–6 years) plus the production-intelligence stack (CERES, Vulcan, BIOME, V3CTOR). Plant-wide data management; 5S, Kanban (~30% lead-time gain), automated core-shop vision.
Modernized the foundry's engineering — simulation-first casting design (Flow3D) and data-based process development; streamlined prototyping to sub-5-day 3D-printed moulds (+50% yield, −60% chills). Root-caused magnesium & copper-alloy defects; achieved AS9100 aerospace certification; design support to Rimac on next-gen electric engines.
Rescued an engine-block launch by root-causing hot tearing with process-parameter changes alone. Built a predictive material-property tool from 10,000+ tensile bars to secure SOP; created an SQL framework to leverage process data across the chain.
Supported a 300,000+ t/yr foundry network; led the most demanding large castings (10-tonne wind-turbine hubs, marine engine components for clients incl. Wärtsilä); cut scrap 50% via simulation + process-data analysis.
The metallurgy & manufacturing foundation behind the data work. Lean Six Sigma Green Belt (2026); AS9100 implementation lead.
Distance runner — consistent road miles and structured blocks. Structured training, measured progress, the long game. The discipline that ships production fixes finishes hard races.
Periodized training, honest data, controlled progression. No single big push — a system that compounds, the same way scrap comes down.
Long road sessions reward pacing, patience and consistency — progress compounds over months of honest mileage, not single heroic efforts.
Open to senior roles in manufacturing data science and production / operations leadership — and consulting — where metallurgy, quality and data meet. If you have scrap, cost or process problems that resist the usual dashboards, let's talk.