Data Scientist

Overview

As a Data Scientist at WSP, you will own end-to-end data and AI solutions for infrastructure clients, from ingestion to production-ready models. You’ll bridge data engineering, analytics and operationalised AI to deliver scalable data products. The role combines data engineering rigor with machine learning to solve real-world infrastructure challenges. You’ll work across disciplines to turn raw data into actionable insights that support climate resilience and asset management.

Pay / Benefits

• nationally significant infrastructure projects
• access to AI and cloud technologies
• flexible working arrangements
• learning and development opportunities
• collaborative and high-performing culture
• opportunity to influence data and AI application

Responsibilities

• Design and build ingestion pipelines from client source systems (SCADA, asset registers, geospatial, climate, environmental and utilities) into cloud platforms (Snowflake, Databricks, Azure)
• Develop robust data architectures with raw, staging and curated models using dbt or equivalent
• Build, validate and deploy statistical and machine learning models into production-ready solutions
• Establish CI/CD practices, environments, secrets management and automation for data and model pipelines
• Assess client data maturity, quality and governance, providing strategic recommendations
• Deliver end-to-end projects from discovery to client handover, including documentation
• Support business development with technical input for proposals and advisory engagements
• Collaborate with multidisciplinary teams across transport, water, energy, defence, environment and major infrastructure projects

Key requirements

• Strong Python development with software engineering practices (packaging, testing, version control, code reviews)
• Advanced SQL with optimization, data modelling and complex transformations
• Production experience with cloud data platforms (Snowflake, Databricks, Azure Data Services; AWS equivalents)
• Experience with dbt, Airflow, Azure Data Factory or similar orchestration tools
• Solid understanding of statistics and ML techniques (regression, classification, forecasting, validation)
• Experience operationalising data products or ML models in production
• Experience handling large, complex, imperfect datasets from multiple sources
• Strong stakeholder engagement and ability to communicate technical concepts to varied audiences

• Stakeholder engagement
• Communication of technical concepts
• Cross-functional collaboration
• Python (software engineering practices)
• SQL (optimization and modelling)
• Snowflake, Databricks, Azure Data Services / AWS technologies

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