[What the role is]
[What the role is]
The team lead for Data Science is responsible for leading the design, development and implementation of advanced analytics models, machine learning models and scalable data pipelines that support the organisation's strategic and operational objectives.
While the primary focus of this role is on data science and advanced analytics, it also encompasses responsibility for ensuring the reliability, scalability and performance of the supporting data infrastructure and pipelines. Working closely with data engineers, data analysts and business stakeholders, the incumbent will oversee the full data lifecycle, from data ingestion and preparation through to model deployment and operational integration, to ensure that analytical solutions are underpinned by robust data engineering practices.
This position plays a pivotal role in driving data-driven innovation, supporting STB's move towards harnessing the full value of our data assets through well-governed, production-ready, and actionable data science solutions.
[What you will be working on]
[What you will be working on]
Main Responsibilities
Leadership and Strategy
Lead, mentor and develop a team of data scientists and data engineers to deliver high-quality, robust end-to-end data and analytics solutions. Collaborate with business teams to identify analytical opportunities and define data science and engineering initiatives aligned with organisational goals. Translate complex business problems into data-driven solutions with measurable outcomes. Establish and maintain best practices for model management, documentation, deployment and maintenance.
Data Science and Advanced Analytics
Design, build and validate predictive, statistical and machine learning models for key business applications such as forecasting, segmentation and optimisation. Oversee model development processes including feature engineering, model training, testing and performance monitoring. Ensure integration of analytical models into business workflows and systems to support data-driven decision making. Stay current with emerging data science methodologies, tools and technologies to drive continuous improvement and innovation in analytical capabilities.
Data Engineering and Pipeline Management
Lead the design, development and maintenance of efficient, scalable and secure data pipelines and ETL/ELT workflows that support analytics, reporting and machine learning operations. Oversee data integration and transformation processes, ensuring high standards of data quality, lineage and governance across ingestion, transformation, and storage layers. Work closely with infrastructure and IT teams to align data engineering solutions with enterprise architecture and cloud strategy. Implement and optimise data orchestration, process automation and monitoring processes to ensure reliability, performance, scalability and cost efficiency of data pipelines.
Collaboration and Communication
Serve as a key liaison between technical teams (e.g. data science, data engineering, analysts) and business teams to ensure alignment on requirements and deliverables. Present analytical insights and technical findings clearly and effectively to both technical and non-technical audiences.
Promote a culture of data excellence, innovation and continuous learning and data-driven decision making within the team and across the organisation.
[What we are looking for]
[What we are looking for]
- Trained in Data Science, Computer Science, Statistics, Engineering or a related field.
- Minimum of 8-10 years of relevant experience in data science, advanced analytics and data engineering.
- At least 3 years experience in a leadership or team lead capacity, managing technical professionals.
- Proven experience in delivering end-to-end analytical solutions, from model design to deployment and maintenance.
- Proven track record of working with cross-functional teams in a data-driven environment.
- Strong time and project management skills.
- Strong analytical and problem-solving capabilities.
- Excellent leadership, communication and stakeholder management skills.
- Collaborative mindset and ability to work effectively in a cross-functional, fast-paced agile environment
- High level of initiative, accountability and attention to detail.
- Proficiency in R, Python, SQL, and distributed data technologies and data warehouse platforms e.g. Spark, Databricks, BigQuery.
- Hands-on experience with ETL/ELT tools and workflow orchestration frameworks e.g. Airflow.
- Strong understanding of machine learning frameworks e.g. scikit-learn, TensorFlow, PyTorch.
- Experience with CI/CD processes, model deployment platforms e.g. MLflow, SageMaker, Vertex AI, and containerisation (Docker, Kubernetes).
- Familiarity with AWS cloud platform and data architecture principles is advantageous.
- Understanding of MLOps and DataOps principles is advantageous.
