Data Analyst Apprentice

You will receive comprehensive training and mentorship, developing your expertise in data collection, cleaning, programming, and communication of data-driven insights. Please note that this is an apprenticeship position and therefore anyone with more than six months professional experience working as a data analyst or who holds a degree or Master's degree in a subject such as Data Science, Business Analytics, Maths will not be eligible. You will also need to commit to completing a Level 4 Data Analyst Apprenticeship. This apprenticeship offers an excellent opportunity to gain hands-on experience and develop practical skills in data science and analytics while working towards a recognised qualification. As a member of the Data Science Lab, you will support a range of research and data analysis projects, contributing to the development of high-quality datasets, analytical tools, and insightful visualisations. This role enables you to learn from experienced data scientists, collaborate across multidisciplinary teams, and apply your skills to projects that inspire better policy and decision-making. You will receive comprehensive training and mentorship, developing your expertise in data collection, cleaning, programming, and communication of data-driven insights. Upon completion, you will have built a solid foundation in modern data science workflows and best practice within a research environment. As well as ensuring sufficient training to meet your Level 4 Data Analyst Apprenticeship, you will have access to a range of complimentary training services as part of RAND's Data Science Lab.

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• 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 policy makers.
  
• 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 the development and upkeep of 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.
  
• Undertake ad hoc duties as required.
  
• 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.
  
• Apply principles of data classification within data analysis activity.
  
• Analyse data sets taking account of different data structures and database designs.
  
• Assess the impact on user experience and domain context on data analysis activity.
  
• Identify and elevate quality risks in data analysis with suggested mitigation or resolutions as appropriate.
  
• Undertake customer requirements analysis and implement findings in data analytics planning and outputs.
  
• Identify data sources and the risks and challenges to combination within data analysis activity.
  
• Apply organisational architecture requirements to data analysis activities.
  
• Apply statistical methodologies to data analysis tasks.
  
• Apply 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 in data.
  
• Collate and interpret qualitative and quantitative data and convert into infographics, reports, tables, dashboards and graphs.
  
• Select and apply the most appropriate data tools to achieve the optimum outcome.
  

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• 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 (e.g. 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.
  
• GCSE: 7 GCSEs (or equivalent) inc Maths and English (grade A‑C / 9‑4 (or equivalent)).
  
• A Level in: Maths, Science, Computer Science or similar (grade A‑C).
  
• COMMUNICATION SKILLS, IT SKILLS, ATTENTION TO DETAIL, ORGANISATION SKILLS, PROBLEM SOLVING SKILLS, PRESENTATION SKILLS, NUMBER SKILLS, ANALYTICAL SKILLS, LOGICAL, TEAM WORKING, CREATIVE, INITIATIVE, NON JUDGMENTAL, PATIENCE.