Senior Director, System Engineering & Intelligence
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NscaleAI data centres
Location: USFull-TimeDirector
Salary$280,000 — $400,000 USD
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Job Details
- Experience
- 15+ years of experience across engineering, asset management, enterprise systems, or industrial analytics, including 7+ years leading cross-functional programmes at director level or above.
- Required Skills
- Machine Learning
Requirements
- Bring 15+ years of experience across engineering, asset management, enterprise systems, or industrial analytics.
- Have 7+ years leading cross-functional programmes at director level or above.
- Have owned a CMMS/EAM platform at scale, such as Maximo, SAP PM, Infor, or Nuvolo.
- Bring depth in master data, PIM/PLM, BOM, and configuration management.
- Have delivered analytics or ML on physical infrastructure, including hands-on experience with BMS/SCADA/historian data.
- Have experience taking models into control or decision loops, including safety, validation, and governance.
- Bring enterprise and data architecture experience spanning ERP, integration, streaming, time-series storage, lineage, and Palantir Foundry or an equivalent platform.
- Have experience with ITIL-aligned operations and controlled MOP, LOTO, and permit-to-work regimes in mission-critical environments.
- Demonstrate credibility with facilities engineers and software teams.
- Have a track record of building a function from scratch.
Responsibilities
- Set a costed, sequenced multi-year roadmap and investment portfolio across CMMS/EAM, PIM/PLM, integration, telemetry, and the data platform.
- Establish canonical asset identity, taxonomy, hierarchy, configuration, and BOM standards, with a system of record for each critical data object.
- Lead data cleansing and validation-at-source programmes to replace spreadsheets with governed lifecycle data.
- Deliver a unified asset and topology model with OT/IT ingest and IEC 62443-aligned segmentation.
- Publish data-quality scores and align shared definitions for PUE, WUE, capacity, stranded power, and availability.
- Own CMMS/EAM configuration baselines and controlled SOP/MOP/EOP libraries; map condition data to assets so predictive maintenance raises work orders.
- Build anomaly detection, remaining-useful-life prediction, and thermal and power optimisation, and progress control loops through defined safety and approval stages.
- Turn L1–L5 commissioning into data, set handover acceptance gates, and quantify readiness by hall and site.
- Benchmark readiness across the portfolio and act as senior escalation point across engineering, procurement, construction, operations, reliability, platform, finance, and HSE.
- Build and develop a team spanning lifecycle engineering, data science, and data engineering.
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