Senior Data Scientist
Role details
Job location
Tech stack
Job description
The Senior Data Scientist will define and execute AI/ML roadmaps, developing and deploying predictive and prescriptive models for use cases like demand forecasting and optimization in infrastructure projects. They will also establish best practices for model lifecycle management, including MLOps, monitoring, and retraining.
Requirements
Data Science, Machine Learning, Python, SQL, MLOps, Azure, Predictive Modeling, Prescriptive Modeling, IoT, Geospatial Analysis, Time-Series Forecasting, Optimization, Causal Inference, A/B Testing, Streaming Data, Anomaly Detection, geospatial, environmental, and operational data. · Support integration of diverse data sources (batch, streaming, IoT) into unified analytics platforms tailored to AECOM's global projects. · Analyse real-time sensor and telematics data to enable predictive maintenance and operational efficiency for connected assets in infrastructure projects. · Implement anomaly detection and streaming inference solutions to improve asset performance and reduce downtime. · Mentor junior data scientists and analysts, fostering a culture of innovation and excellence in analytics and modelling. · Promote best practices in data science and analytics, ensuring alignment with AECOM's quality standards and project delivery frameworks. ·Present work outputs to both technical and non-technical audiences, translating complex analytics and AI/ML concepts into clear, layman's terms. Qualifications Minimum Requirements · 3-5+ years of experience in data science or applied machine learning, preferably in infrastructure, environmental, or urban development sectors. · Strong proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow) and SQL, with experience in geospatial and environmental data analysis. · Experience with MLOps tools (MLflow, Docker, CI/CD pipelines) and cloud platforms (Azure preferred), ensuring scalable and reliable solutions. · Proven ability to influence non-technical stakeholders and communicate complex concepts clearly, especially in the context of infrastructure and environmental projects. · Experience mentoring and coaching technical teams, promoting collaboration and innovation. Preferred Qualifications · Master's degree in Computer Science, Statistics, Applied Mathematics, or related field, with a focus on data science applications in infrastructure or environmental domains. · Familiarity with time-series forecasting, optimisation, and causal inference, particularly in project planning and resource management. ·