Director of Yield Intelligence & Semiconductor Analytics

Location: Europe (Remote / Hybrid) The Role yieldWerx is building the next-generation yield, quality, and traceability intelligence platform for semiconductor manufacturing. We are not hiring a traditional product manager. We are looking for a senior yield engineer / integration leader who has owned production yield, built analytical models, and understands how semiconductor physics, test systems, and data architectures interact. This role exists to: • Translate real manufacturing pain into scalable platform capabilities • Architect outlier detection and yield intelligence frameworks • Define screening, SPC, genealogy, and cross-stage analytics strategies • Shape the roadmap for AI-assisted yield optimization • Serve as technical authority in front of Tier 1 customers If you have never personally owned yield KPIs in production, this is not the role. What You Will Own 1. Yield Strategy Architecture • Define screening philosophy (PAT, GDBN, multivariate, statistical bin limits) • Establish false-positive vs escape tradeoff frameworks • Translate automotive-grade quality needs into product logic 1. Cross-Stage Correlation Vision • Connect process, wafer sort, assembly, final test, and field data • Define genealogy-based risk analysis • Architect cross-die and cross-lot learning mechanisms 1. Data & Analytics Direction • Shape ingestion validation philosophy (STDF, WAT/PCM, KLARF, MES) • Define data enrichment & version control strategy • Identify where AI adds value vs where deterministic logic is superior 1. Customer Authority • Explain architecture to IDMs, OSATs, fabless leaders • Challenge customers’ current YMS thinking • Represent yieldWerx at technical engagements Required Background We are looking for someone who has: • 10+ years in semiconductor manufacturing • Owned production yield KPIs (not just supported them) • Worked across wafer sort and final test • Hands-on experience with SPC, screening, and yield ramp • Experience debugging yield excursions in production • Built or customized analytics tools (JMP, Python, internal tools) • Worked with STDF, WAT/PCM, defect data, or similar raw formats • Participated in NPI or technology ramp Bonus: • Automotive/AEC exposure • Integration engineer experience • Advanced packaging or heterogeneous integration exposure • Experience challenging or replacing legacy YMS systems