The ideal candidate will be a strategic thinker with extensive experience in leveraging data analytics, machine learning, and AI to drive innovation and optimize operations in construction projects. If you have a proven track record of delivering data-driven solutions and possess strong leadership skills, we invite you to join our dynamic team and shape the future of data science in the construction sector.
Responsibilities:
Strategic Leadership:
- Define and execute the data science strategy to address business challenges and opportunities in the building and construction industry as part of the Digital Technology Solutions (DTS) team, working closely with the Meinhardt smart cities and sustainability teams as well.
- Lead and inspire a team ofdata scientists, analysts, and engineers to achieve strategic objectives
Data Strategy and Governance:
- Develop and implement data governance policies and procedures to ensure data quality, integrity, and security.
- Establish data architecture and infrastructure to support analytics initiatives and ensure scalability.
- Provide advice on data governance and management for the rest of the Meinhardt Group, as the need arises.
Advanced Analytics and Modelling:
- Drive the development of advanced analytics models and algorithms to optimise construction processes, resource allocation, and project management.
- Identify opportunities for predictive analytics and prescriptive recommendations to enhance decision-making, especially for building and construction domains such as scheduling, asset/facilities management, resource management, and safety.
Innovation and Research:
- Stay abreast of emerging technologies, trends, and best practices in data science, AI, and machine learning.
- Lead DTS’s development of Artificial Intelligence and Machine Learning (AI/ML) applications: including but not limited to Generative AI, Large Language Models (LLMs), and Autonomous Agents or Chatbots.
- Foster a culture of innovation and experimentation to explore novel approaches and solutions, including setting up research priorities and workloads for data science team and structuring training/development.
Cross-functional Collaboration:
- Collaborate closely with stakeholders across departments, including software engineering, project management, and business development, to identify data-driven opportunities and challenges.
- Partner with IT and engineering teams to integrate data science solutions into existing systems and workflows.
Data Visualisation and Communication:
- Communicate complex data science concepts and insights to non-technical stakeholders through clear and compelling visualisations and presentations.
- Translate business requirements into actionable data science projects and initiatives.
Performance Monitoring and Optimization:
- Establish key performance indicators (KPIs) and metrics to measure the impact of data science initiatives on business outcomes.
- Continuously monitor and optimize models and algorithms to ensure accuracy, relevance, and effectiveness.
Qualifications:
- Advanced degree (Ph.D. or Master’s) in Data Science, Computer Science, Statistics, or a related field.
- Extensive experience (5+ years) in data science, machine learning, and AI, with a focus on applications in the building and construction industry.
- Proven track record of leadership and strategic decision-making in a data-driven environment.
- Strong programming skills in languages such as Python, R, or Scala.
- Expertise in machine learning frameworks and libraries (e.g., TensorFlow, scikit-learn, PyTorch).
- Proficiency in data visualisation tools and techniques (e.g., Tableau, Matplotlib, D3.js).
- Excellent communication, collaboration, and stakeholder management skills.
- Experience working with large-scale datasets and distributed computing frameworks (e.g., Hadoop, Spark) is a plus.
Preferred Qualifications:
- Experience with construction/infrastructure industry data and processes.
- Exposure to geospatial data analysis.
- Knowledge of data engineering and data pre-processing techniques.
- Familiarity with cloud platforms (e.g., AWS, Azure, or Google Cloud).
- Understanding of statistical analysis and hypothesis testing.
