At a Glance
- Tasks: Join a dynamic team to analyse data and develop machine learning solutions.
- Company: QinetiQ, an inclusive tech company focused on data science.
- Benefits: Competitive salary, flexible working, coaching, and 25 days holiday.
- Other info: Opportunities for growth and collaboration with experts across the business.
- Why this job: Gain hands-on experience in data science while making a real impact.
- Qualifications: A-Level Maths and programming experience in Python or similar.
The predicted salary is between 27150 - 27150 € per year.
At QinetiQ we are creating a workplace that is inclusive; where our differences are not only embraced but make us stronger. A place where we can connect with each other and benefit from the experiences and thinking from people with varied backgrounds, and at different stages in their careers.
About the team
Data Science & Engineering: Our role is to ensure our customers gain the maximum advantage from their data. We are involved at all stages of the data lifecycle from the initial gathering & processing stage, through the analysis phase, leading to actionable outputs & advice.
The Data Science and Engineering teams within Software Engineering, Communication Networks & Data Science (SECNDS) discipline consist of a mix of data scientists (exploring data sets and algorithms) and data engineers (building the infrastructure to capture and process the data). The team’s skills however are varied and cover a wide range of disciplines. Our daily work involves applying both conventional and novel machine learning techniques to customer problems as appropriate to advise on and/or demonstrate the opportunities created through exploiting their data.
What will I be doing?
The team works across all data types, everything from numerical data to natural language processing and signals analysis through to imagery interpretation. Where necessary, we also collect or simulate data using mathematical models.
A typical day will see our apprentice working as part of small project teams. You will attend project meetings, be involved in data preparation, and applying the relevant Machine Learning or Artificial Intelligence techniques. You will over time, be expected to code up solutions to support this work, and to integrate with the team’s coding best practices. You may occasionally be involved in stakeholder engagements or presentations, and you will often help with the report writing process. Your days can have a mixture of on site and home working, depending on the specific project’s data requirements.
We frequently collaborate with colleagues and subject experts across the business to gain cross-domain insight to support our work.
In this role you will gain practical experience in the full end to end data pipeline from data collection, data wrangling and modelling through to generating conclusions and results. You will gain knowledge of statistical techniques such as supervised and unsupervised machine learning algorithms, develop programming skills in Python and for the Cloud, as well as general data analysis techniques. You will also gain soft skills such as planning, technical report writing, and presenting.
Apprenticeship details:
- Title: Level 6 Data Scientist Apprenticeship
- Qualification: BSc (Hons) Data Science
- Course provider: Cranfield
- Academic requirements: Grade 5 in GCSE Mathematics or equivalent, Grade 4 in GCSE English Language or equivalent (prior to admission) with BBC at A-Level to include Maths. We will not accept A-Levels Citizenship Skills, General Studies, and Critical Thinking or Level 4 Data Analyst apprenticeship at Merit or Distinction.
Additional requirements:
- You must be able to travel to the training provider for the academic elements of the Level 6 Apprenticeship. Expenses for travel will be reimbursed in line with our expenses policy.
- Some understanding of statistics (statistical analysis) and/or mathematical modelling.
- Some experience of programming in at least one coding language e.g. Python, R, C++.
- Some familiarity or experience with basic coding best practices e.g. version control and code quality.
Beneficial:
- Experience with any cloud computing platform.
- Machine learning or data analysis project experience (through academia or personal projects).
- Evidence of soft skills, including examples of working in a team, technical communication (written or oral).
How to apply: Please fill in the application and include both a CV and a covering letter.
Our Benefits: On demand learning, access to courses, modules, and lectures via multiple digital learning platforms, Coaching and Mentoring, 25 days annual holiday excluding bank holiday, Matched contribution pension scheme, with life assurance, Flexible Benefits package, Employee discount portal, Employee Assistance Programme, Employee-led networks.
Security: Many of our roles at QinetiQ are subject to national security vetting. Applicants who already hold the appropriate level of vetting may be able to transfer it upon appointment, subject to approval. Many roles are also subject to restrictions on access to information, which means factors such as nationality, previous nationalities held and the country in which you were born may impact your role. Please note that all applicants for this role must be eligible for SC clearance, as a minimum.
Recruitment Process: We want to make sure that our recruitment process is as inclusive as possible and we aspire to bring out the best in our candidates by creating an environment where everyone feels valued, heard and supported. If you have a disability or health condition that may affect your performance in certain assessment types, please speak to your recruiter about potential reasonable adjustments.
QinetiQ is a place where you’ll be able to make a real difference. You’ll be part of an inclusive culture that values diversity, rewards integrity and merit, and where you’ll be empowered to fulfil your potential. We welcome candidates from all backgrounds, come and be part of our team!
Closing date for new applicants: 22nd May 2026.
Data Scientist Apprentice in Malvern employer: QinetiQ Security & Defense Contractors
At QinetiQ, we pride ourselves on fostering an inclusive and collaborative work environment where diverse perspectives are valued. As a Data Scientist Apprentice in Malvern, you will benefit from extensive learning opportunities, mentorship, and a flexible work culture that encourages personal and professional growth. With access to cutting-edge projects and a supportive team, you'll be empowered to make a meaningful impact while developing your skills in data science and engineering.
Contact Detail:
QinetiQ Security & Defense Contractors Recruiting Team
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist Apprentice in Malvern
✨Tip Number 1
Network like a pro! Reach out to current employees at QinetiQ on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for landing the Data Scientist Apprenticeship. Personal connections can make a huge difference!
✨Tip Number 2
Prepare for interviews by brushing up on your technical skills. Make sure you can talk confidently about machine learning techniques, Python programming, and data analysis. We want to see your passion for data science shine through!
✨Tip Number 3
Showcase your projects! If you've worked on any data science or machine learning projects, whether in school or on your own, be ready to discuss them. We love seeing practical applications of your skills, so bring your A-game!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re serious about joining our team at QinetiQ. Let’s get you started on this exciting journey!
We think you need these skills to ace Data Scientist Apprentice in Malvern
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Scientist Apprentice role. Highlight relevant skills like programming in Python and any experience with machine learning or data analysis projects. We want to see how your background fits with what we do!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data science and how you can contribute to our team. Be sure to mention any soft skills, like teamwork and communication, that you've developed.
Showcase Your Projects:If you've worked on any personal or academic projects related to data science, make sure to include them in your application. We love seeing practical examples of your work and how you approach problem-solving!
Apply Through Our Website:Don't forget to apply through our website! It’s the best way to ensure your application gets to us directly. Plus, it shows you're serious about joining our team at QinetiQ!
How to prepare for a job interview at QinetiQ Security & Defense Contractors
✨Know Your Data Science Basics
Brush up on your understanding of statistical techniques and machine learning algorithms. Be ready to discuss how you've applied these concepts in your studies or projects, as this will show your potential employer that you have a solid foundation for the role.
✨Showcase Your Coding Skills
Familiarise yourself with Python and any other programming languages you’ve used. Prepare to talk about your coding experiences, including any projects where you implemented coding best practices like version control. This will demonstrate your technical capabilities and readiness for the apprenticeship.
✨Prepare for Teamwork Questions
Since collaboration is key in this role, think of examples from your past experiences where you worked effectively in a team. Highlight your communication skills and how you contributed to achieving a common goal. This will help illustrate your fit within their inclusive culture.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions! Inquire about the team dynamics, the types of projects you might work on, or how they support apprentices in their learning journey. This shows your genuine interest in the role and helps you assess if it’s the right fit for you.