Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Science Apprentice

RAND Europe
Cambridge
1 day ago
Create job alert

RAND Europe is an independent, not-for-profit research organisation whose mission is to help improve policy and decision-making through rigorous and independent research and analysis. We benefit the public interest through the impact and wide dissemination of over 200 projects per year. Our work at its most impactful changes policy, practice and process to the benefit of the public good. That is also our organisational mission.


About The Role

This Data Science Apprentice role offers an excellent opportunity to gain hands‑on experience and develop practical skills in data science and analytics while working towards a recognised qualification on a two‑year fixed‑term contract. 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 the Level 4 Data Analyst Apprenticeship, you will have access to a range of complimentary training services as part of RAND's Data Science Lab.


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 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

Requirements

  • 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
  • A‑levels (or equivalent) in Mathematics, Science, Computing or related subjects (essential)
  • GCSE Maths and English A*‑B/5‑9 (essential)
  • Further experience or coursework in coding, statistics, or data analysis (desirable but not required)

Benefits

  • Pension – 8% Employer contribution
  • 33 days holiday allowance, including the Bank Holidays
  • Annual salary review
  • BUPA medical insurance
  • Generous company sick pay
  • Enhanced family friendly policies
  • Group income protection scheme
  • Group life assurance
  • Compassionate leave
  • Flexible working arrangements
  • Learning and development opportunities
  • Employee wellbeing training and support
  • Fresh fruit every day
  • Free on‑site parking
  • Cycle to work scheme
  • Access to company bikes
  • Service awards

Salary

Circa £21,000


How To Apply

If you believe you are suited to the above role, please follow the link here to place your application via Digital Native: Data Analyst Apprentice - Digital Native


Please note if you apply via Workable your application may not be reviewed. If you have not been contacted within 30 days of the application deadline, please assume your application has not been successful.


#J-18808-Ljbffr

Related Jobs

View all jobs

Level 6 Data Science

Data Analyst Apprenticeship

Data Engineer Apprentice

Data Analyst Apprenticeship

Data Analyst Apprenticeship

Data Engineer Apprentice

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.