Graduate Data Scientist
Role details
Job location
Tech stack
Job description
The following duties and responsibilities form part of the role, and you will receive full training, guidance, and support to develop the skills needed to carry them out effectively. As an apprentice, you won't be expected to know everything from day one - you'll learn gradually through hands-on experience, mentoring from the team, and structured training as you grow into the role.
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Data Exploration and Analysis: Conduct exploratory data analysis (EDA), investigating trends, patterns, and anomalies in diverse datasets sourced from across the Group.
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Model Development: Assist in the design, development, and validation of statistical models and machine learning algorithms to support forecasting, optimisation, anomaly detection, and operational efficiency.
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Feature Engineering and Preparation: Prepare, clean, and transform data for modelling purposes, ensuring datasets are structured and optimised for analytical accuracy and performance.
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Insights and Visualisation: Translate analytical findings into clear, meaningful insights and visualisations that support business decision-making, ensuring results are accessible to both technical and non-technical audiences.
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Collaboration and Cross-Functional Support: Work closely with Data Engineers, Analysts, and business stakeholders to understand analytical requirements and contribute to data-driven solutions aligned with Group objectives.
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Research and Innovation: Maintain awareness of emerging Data Science methodologies, technologies, and industry trends-particularly in areas relevant to utilities, construction, and energy transition-and apply this knowledge to enhance analytical approaches.
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Model Monitoring and Continuous Improvement: Support the deployment, monitoring, and iterative refinement of predictive models to ensure sustained accuracy and relevance.
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Machine Learning Lifecycle Management: Support the full lifecycle of machine learning models, including versioning, experiment tracking, performance monitoring, and iterative improvement to ensure models remain accurate and reliable over time.
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Off-the-Job Training: Participate in structured off-the-job learning, including theoretical training, practical exercises, and exposure to industry best practices.
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Graduate Programme Participation: Engage fully in the Graduate Programme, combining hands-on data science experience with formal training to meet statutory and programme requirements.
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Development Standards: Follow established OCU Data Team development standards, ensuring analytical work, scripts, notebooks, and models are appropriately documented and source controlled.
Requirements
Do you have experience in Spark?, Desirable:
- Knowledge of statistical concepts, data analysis techniques, and basic machine learning principles.
- A genuine interest in data science and strong commitment to ongoing professional development. Problem-solving ability with a logical, analytical mindset. Strong attention to detail, ensuring high-quality data preparation and model validation. Effective communication skills, including the ability to present complex insights in a clear and understandable way. Ability to work collaboratively as part of a multi-disciplinary team. Familiarity with data analysis or coding tools (such as Python, SQL, R, or relevant libraries). Awareness of source control tools (e.g. Git).
- Familiarity with cloud-based data platforms and tools such as Microsoft Azure, Databricks, Apache Spark, or related technologies, with an interest in developing practical skills in modern scalable data processing environments.