Principal Data Scientist (Mississauga - Head Office)

KenWave Solutions; Mississauga; On-site; CAD 140,000 - 210,000; Full time

Job summary

KenWave Solutions is seeking a Principal Data Scientist to lead analytics, ML, signal processing, and AI across the DRI platform. You will set the technical vision and roadmaps, and act as senior authority on production analytics methods.

You will mentor data scientists, data engineers, and analysts while collaborating with Product, Engineering and Operations to translate research into deployable capabilities. This role is on-site in Mississauga, Canada.

Qualifications

• PhD or Master's degree in a quantitative field.
• 10+ years in advanced analytics, ML, or computational research.
• Experience leading high-impact technical projects.
• Strong mentoring and team development skills.

Responsibilities

• Define and execute data science strategy and roadmaps.
• Lead design of analytics and AI components for DRI platform.
• Mentor data scientists, analysts, and engineers.
• Collaborate with product, engineering, and operations to deploy research into production.

Skills

Python

Machine Learning

Signal Processing

Time-Series Analysis

Statistical Modelling

Education

PhD or Master’s in quantitative field

Tools

Scikit-Learn

PyTorch

Git/GitHub

Cloud Platforms (AWS/Azure)

MLOps

Job description

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 Responsibilities

Technical 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

Lead R&D initiatives with

• 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