Principal Data Scientist at KenWave Solutions — NeverHard

About KenWave KenWave Solutions Inc. is transforming the way critical infrastructure is inspected and managed. Our patented Dynamic Response Imaging™ (DRI™) technology combines vibroacoustic sensing, advanced signal processing, data analytics, and machine learning to assess the condition of pressurized pipelines without taking them out of service. As part of Obayashi, KenWave is building the next generation of infrastructure intelligence platforms for water, industrial, and energy pipeline owners worldwide. The Opportunity We are seeking a Principal Data Scientist to lead the development of advanced analytics, machine learning, signal processing, and AI capabilities that form the foundation of KenWave's technology roadmap. This is a rare opportunity to work at the intersection of: Physics Acoustics and vibration Machine learning Digital twins Infrastructure intelligence Signal processing Large-scale data analytics The successful candidate will be the senior technical authority for data science and analytical algorithms, helping transform KenWave from a project-based analytics organization into a scalable technology platform company. Key ResponsibilitiesTechnical Leadership Define and execute KenWave's data science and AI strategy. Establish long-term analytics and machine learning roadmaps. Lead the design of next-generation DRI™ analytical methods. Guide architecture decisions for analytics and AI components within the DRI platform. Mentor Data Scientists, Data Analysts, and Data Engineers. Technical Governance & Methodology Oversight Serve as the technical owner and final technical authority for KenWave’s production analytical methods, algorithms, statistical and machine learning models, and associated validation methodologies. Establish technical standards, design principles, development practices, and acceptance criteria for the Data Science, Algorithms and Analytics function Review and approve changes to production analytical methodologies and algorithms prior to release Signal Processing & Advanced Analytics Develop and enhance algorithms for: Vibroacoustic analysis Spectral analysis Modal analysis Time-frequency analysis Structural response characterization Anomaly detection Design robust feature extraction frameworks from field-collected waveforms. Improve pipeline condition assessment accuracy and repeatability. Design and configure robust metadata tracing and analytics output standards Design and develop distributable software within scalable environments Machine Learning & AI Lead development of machine learning models supporting: Condition classification Pipe deterioration assessment Leak detection Predictive infrastructure maintenance Risk scoring Evaluate modern AI approaches including: Deep learning Physics-informed machine learning Bayesian models Foundation models Agent-based analytics Establish reproducibility, traceability, version control, documentation, and testing standards for production algorithms and models Product and Commercialization Work closely with Product, Engineering, Operations, and Executive teams. Translate research concepts into deployable customer-facing capabilities. Support the transition toward automated and AI-assisted condition assessment. Contribute to patent development and intellectual property creation. Research and Innovation Universities Research organizations Industry partners Government-funded programs Publish technical papers and support conference presentations. Identify emerging technologies that may create competitive advantage. Required Qualifications Education: Data Science Computer Science Engineering Applied Mathematics Physics Signal Processing Vibro-Acoustics Related quantitative discipline PhD or Master's degree in one of: Experience 10+ years in advanced analytics, machine learning, or computational research. Experience leading high-impact technical projects. Experience mentoring and developing technical teams. Technical Skills Strong experience in: Python Machine Learning Statistical Modelling Signal Processing Time-Series Analysis Experience with: Scikit-Learn PyTorch Git/GitHub Cloud Platforms (AWS/Azure) Data Engineering Pipelines MLOps Strong understanding of: SQL Experimental design Statistical inference Optimization Algorithm development Preferred Qualifications The following are considered significant assets: Vibroacoustics Acoustics & vibration engineering Pipeline assessment Non-destructive testing (NDT) Utilities infrastructure Water industry Oil & gas pipeline monitoring Finite Element Analysis (FEA) Physics-informed machine learning Digital twins Geospatial analytics This is a non-management position #J-18808-Ljbffr