Data Scientist – End‑to‑End Solutions

About the role

WSP is seeking a Data Scientist with strong data engineering capabilities to design, build and deploy scalable data and machine learning solutions for infrastructure clients. The role focuses on end‑to‑end delivery, bridging data engineering, analytics and operationalised AI.

Key responsibilities

• Design and build ingestion pipelines from client source systems (SCADA, asset registers, geospatial, climate, environmental and utilities data) into cloud platforms such as Snowflake, Databricks and Azure.
• Develop layered data architectures (raw, staging, curated) using dbt or equivalent frameworks.
• Build, validate and deploy statistical and machine learning models, turning prototypes 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 through to client handover and support business development activities.

Required profile

• Strong Python development skills with software‑engineering best practices (packaging, testing, version control, code reviews).
• Advanced SQL expertise, including optimisation, data modelling and complex transformations.
• Production experience with cloud data platforms such as Snowflake, Databricks, Azure Data Services or equivalent AWS technologies.
• Experience with modern orchestration tools like dbt, Airflow or Azure Data Factory.
• Solid understanding of statistics and machine‑learning techniques (regression, classification, forecasting, validation).
• Proven ability to operationalise data products, ML models or pipelines in production environments.

Required skills

• Python
• SQL
• Snowflake
• Databricks
• Azure (Data Services, Functions)
• dbt
• Airflow
• Azure Data Factory
• MLflow
• Docker
• Terraform or Bicep
• Power BI
• GeoPandas

What we offer

• Opportunity to work on nationally significant infrastructure and sustainability projects.
• Access to industry‑leading digital, AI and cloud technologies.
• Flexible working arrangements supporting work‑life balance.
• Ongoing investment in learning, development and technical career growth.
• Collaborative, inclusive and high‑performing culture encouraging innovation.