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.
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.
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. The system that scales the work.
Analytical data cube + relationship graph + automated 10-pillar governance audit + SPC. Surfaced a major hidden cost variance 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.
Realigned from foundry management to a deep-analysis / data expert track. Owns the manufacturing decision loop — the 5M+ CHF Closed-Loop Quality Intelligence program plus the production-intelligence stack (CERES, Vulcan, BIOME, V3CTOR). Plant-wide data management; 5S, Kanban (~30% lead-time gain), automated core-shop vision.
Led the transformation to AS9100 aerospace certification. Embedded simulation as a core design tool (+50% yield, −60% chills, sub-5-day prototypes); root-caused magnesium & copper-alloy defects; 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 with a fastpacking habit — multi-day trail on a light pack. 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.
Fastpacking rewards judgment under fatigue and light, deliberate kit. Multi-day problems solved by preparation, not improvisation.
Open to senior data-science roles and consulting where metallurgy, quality and data science meet. If you have scrap, cost or process problems that resist the usual dashboards — let's talk.