Data Analyst Apprentice
Rand
- Wales, England, United Kingdom
- Wales, England, United Kingdom
À propos
Responsibilities
Support the extraction, aggregation, and creation of datasets from a range of sources, including open databases, web scraping, policy documents, academic literature, and bibliometric data.
Clean, standardise, and prepare datasets from various sources, ensuring data quality and consistency prior to analysis.
Explore and analyse datasets using a range of analytical tools – including statistical methods, regression analysis, and machine learning techniques – to identify key trends and generate actionable insights for research projects.
Create clear, engaging data visualisations and dashboards to communicate key research insights to internal and external audiences, including policymakers.
Contribute to the adaptation of existing data workflows, such as the systematic application of large language models (LLMs) in a Python programming environment for data extraction and analysis.
Maintain up‑to‑date code repositories and documentation, ensuring code is well annotated and accessible for team use.
Assist in developing and maintaining dashboards and digital observatories using tools such as Power BI, Streamlit, Shiny, Plotly, or WordPress.
Collaborate across the Data Science Lab and research groups, providing support to colleagues and contributing to a positive, inclusive team environment.
Use data systems securely to meet requirements and in line with organisational procedures and legislation, including principles of Privacy by Design.
Implement the stages of the data‑analysis lifecycle, applying principles of data classification within data analysis activities.
Analyse data sets taking account of different data structures and database designs, assessing the impact on user experience and domain context.
Identify and elevate quality risks in data analysis, providing suggested mitigation or resolutions as appropriate.
Undertake customer requirements analysis and implement findings in data analytics planning and outputs.
Identify data sources and assess risks and challenges to combination within data analysis activity.
Apply organisational architecture requirements to data analysis activities, applying statistical methodologies and predictive analytics in the collation and use of data.
Collaborate and communicate with a range of internal and external stakeholders, using appropriate styles and behaviours to suit the audience.
Use a range of analytical techniques such as data mining, time‑series forecasting, and modelling techniques to identify and predict trends and patterns.
Collate and interpret qualitative and quantitative data, converting it into infographics, reports, tables, dashboards, and graphs.
Select and apply the most appropriate data tools to achieve the optimum outcome.
Qualifications
Strong interest in data science and research analytics, with demonstrable motivation to build a career in this field.
Familiarity with data analysis, statistical concepts, and creating data visualisations (coursework, science experiments, projects, or self‑study count).
Some experience with coding (Python, R, or similar) is desirable but not essential.
Excellent problem‑solving skills.
Effective verbal and written communication skills, with the ability to present findings clearly.
Strong team player who can work collaboratively and communicate clearly within a team.
Self‑starter with a positive attitude, curious mindset, and willingness to embrace new challenges.
Commitment to continuous learning and professional development.
Education: 7 GCSEs (or equivalent) including Maths and English (grade A‑C / 9‑4); A Level in Maths, Science, Computer Science or similar (grade A‑C).
Share if you have other relevant qualifications and industry experience.
Soft skills: Communication, IT, attention to detail, organisation, problem‑solving, presentation, number, analytical, logical, team working, creative, initiative, non‑judgemental, patience.
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Compétences linguistiques
- English
Avis aux utilisateurs
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