Job Specifications
Help grow a safer, cleaner, healthier future for everyone, every day.
Role Responsibilities
Data Preparation & Quality:
Collect, clean, preprocess, and validate data from multiple sources (e.g. sensors, systems, files, APIs).
Check data quality, consistency, completeness, and basic integrity issues.
Support data documentation to ensure datasets are understandable and reusable.
Assist in maintaining reproducible and well-structured datasets.
Data Analysis & Insight Generation
Perform exploratory data analysis (EDA) to identify trends, patterns, anomalies, and data issues.
Produce clear analytical outputs that support decision-making and experimentation.
Translate analytical findings into simple, understandable insights for technical and non-technical stakeholders.
AI / Machine Learning Support
Support the AI/Data Science Specialist in:
Building and testing ML pipelines.
Preparing features for different ML problem types (e.g. time series, tabular, image data).
Running experiments and comparing model results under guidance.
Research and summarise different model approaches and methodologies for defined AI problems.
Apply and compare appropriate evaluation metrics for different AI/ML tasks (e.g. classification, regression, forecasting).
Assist with validating model outputs and checking for data or modelling issues.
Note: This role does not own production models but contributes to experimentation, validation, and learning.
AI Tools & Technology Exploration
Explore and evaluate off-the-shelf AI tools and platforms for defined use cases.
Support assessments of AI solutions both as:
A provider (internally developed solutions), and
A consumer (third-party tools or vendors).
Document findings, limitations, and risks of tools tested.
AI Governance, Risk & Best Practice
Support adherence to AI and data best-practice principles across projects.
Assist in:
Ensuring data and model quality standards are met.
Reducing risks associated with AI adoption in line with AI policy and governance frameworks.
Help document decisions, assumptions, and limitations to support responsible AI use.
Visualisation & Communication
Create clear data visualisations (charts, tables, dashboards) to communicate insights effectively.
Present findings to the AI specialist and Digital Solutions team in a structured and understandable way.
Support preparation of internal reports, presentations, and technical summaries.
Collaboration & Learning
Work closely with the AI/Data Science Specialist and other Digital Solutions team members.
Actively learn from feedback, code reviews, and project retrospectives.
Contribute to a collaborative, experiment-driven team culture.
Essential
Required Skills & Experience
Degree (or equivalent experience) in Data Science, Computer Science, Engineering, Mathematics, Statistics, or a related field.
1-2 years experience in relevant data analysis roles.
Basic experience with:
Data analysis and manipulation (e.g. Python, pandas, NumPy, or similar).
Data visualisation tools (e.g. matplotlib, seaborn, Power BI, Tableau, or equivalent).
Understanding of fundamental ML concepts (e.g. supervised vs unsupervised learning).
Strong analytical thinking and attention to detail.
Ability to explain findings clearly in plain language.
Highly organised and methodical, with strong attention to detail.
Proven ability to maintain clean, well-documented, and reproducible work.
Comfortable working within defined standards for documentation, data management, and governance.
Able to manage multiple tasks while keeping analysis, data, and documentation well structured.
Desirable
Experience in AI & Data solutions including practical experience of implementing these technologies to build productised solutions.
Exposure to ML workflows, model evaluation, or experimentation.
Experience with time series, image, or tabular data (academic or project-based).
Familiarity with version control (e.g. Git).
Interest in AI governance, ethics, or responsible AI practices.
Professional attributes
Strong curiosity and willingness to learn, with a genuine interest in data, AI, and emerging technologies, and motivation to apply them to real-world business and operational problems.
Proactive and “can-do” mindset – organised, resilient, and pragmatic when working through analytical or data challenges. Comfortable asking questions, seeking feedback, and iterating towards better solutions under guidance.
Analytical and detail-oriented – able to work carefully with data, identify issues, and apply logical thinking to support evidence-based decisions. Comfortable breaking down complex problems into structured analytical tasks.
Well organised with strong documentation habits – able to maintain clear, structured documentation, good data and project housekeeping, and reproducible analysis. Pays attention to detail in data handling, versioning, and record keeping.
Hands-on and practical – enjoys working directly with data, tools, and code, and is comfortable