Senior Data Scientist

Overview

As a Senior Data Scientist in AECOMs Data Science & Analytics team, you will drive data-driven decision making across global infrastructure projects. You’ll advance AI/ML strategies and IoT-driven insights to unlock measurable value in environmental, urban development, and infrastructure domains.

You will design and deploy predictive and prescriptive models, supervise model lifecycle and collaborate with cross-functional teams to integrate data science into operations. This role offers the chance to influence large-scale projects and advance sustainable solutions within a world-class firm.

Pay / Benefits

• hybrid work options
• competitive compensation and well-being programs
• global firm with diverse opportunities
• inclusive culture with emphasis on equity, diversity and inclusion
• transformational work with big impact
• employee growth and development programs

Responsibilities

• Define and execute AI/ML roadmaps aligned with business objectives, emphasizing sustainability and efficiency
• Develop and deploy predictive and prescriptive models for use cases like demand forecasting, optimization, and anomaly detection in infrastructure
• Establish best practices for model lifecycle management (MLOps, monitoring, retraining)
• Extract actionable insights from complex datasets to inform strategic decisions across infrastructure, environment, and urban development
• Apply statistical modelling, machine learning, and optimization to solve high-impact business problems (resource allocation, risk management, asset lifecycle forecasting)
• Design and run experiments (A/B tests, causal inference) to measure impact of data initiatives on project outcomes
• Collaborate with data engineering to build feature pipelines and ensure data quality from diverse sources (geospatial, environmental, operational)
• Support integration of batch, streaming, and IoT data into unified analytics platforms for global projects
• Analyze real-time sensor and telematics data for predictive maintenance and operational efficiency
• Implement anomaly detection and streaming inference to improve asset performance and reduce downtime
• Mentor junior data scientists and analysts, fostering innovation and excellence
• Promote data science best practices in line with quality standards and project delivery frameworks
• Present complex analytics and AI/ML concepts to both technical and non-technical audiences

Key requirements

• 3–5+ years in data science or applied machine learning, preferably in infrastructure or environmental sectors
• Strong Python (pandas, scikit-learn, PyTorch/TensorFlow) and SQL skills, with geospatial and environmental data experience
• Experience with MLOps tools (MLflow, Docker, CI/CD) and cloud platforms (Azure preferred)
• Ability to influence non-technical stakeholders and communicate complex concepts clearly in infrastructure and environmental contexts
• Experience mentoring technical teams and promoting collaboration and innovation
• communication
• stakeholder engagement
• mentoring
• Python (pandas, scikit-learn, PyTorch/TensorFlow)
• SQL
• MLOps (MLflow, Docker, CI/CD)

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