At a Glance
- Tasks: Join a dynamic team to analyse data and apply machine learning techniques.
- Company: QinetiQ, an inclusive tech company focused on data science.
- Benefits: Competitive salary, flexible working, coaching, and 25 days holiday.
- Other info: Exciting career growth with access to on-demand learning and mentoring.
- Why this job: Gain hands-on experience in data science while making a real impact.
- Qualifications: GCSE Maths and English, programming skills 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. Over time you will 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
- Provider Link: https://www.cranfield.ac.uk/mku/mku-data-scientist
Academic requirements:
Grade 5 in GCSE Mathematics or equivalent, Grade 4 in GCSE English Language or equivalent (prior to admission) with GCSE 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).
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.
Data Scientist Apprentice in Malvern employer: QinetiQ
Contact Detail:
QinetiQ Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist Apprentice in Malvern
✨Tip Number 1
Get your networking game on! Connect with professionals in the data science field on LinkedIn or at local meetups. We can’t stress enough how valuable it is to have a chat with someone already in the industry; they might just point you towards opportunities that aren’t even advertised!
✨Tip Number 2
Practice makes perfect! Work on personal projects or contribute to open-source ones to showcase your skills. This not only boosts your portfolio but also gives you real-world experience that you can talk about during interviews. Plus, we love seeing passion and initiative!
✨Tip Number 3
Prepare for those interviews! Research common data science interview questions and practice your answers. We recommend doing mock interviews with friends or mentors to build confidence. Remember, it’s not just about technical skills; showing your soft skills is equally important!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always looking for fresh talent like you, so don’t hesitate to hit that apply button and show us what you’ve got!
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 any relevant experience, skills, and projects that align with the job description. We want to see how you can contribute to our team!
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 your background makes you a great fit for us. Be genuine and let your personality come through.
Showcase Your Skills: Don’t forget to mention your programming skills, especially in Python or any other coding languages you know. If you've worked on any data analysis or machine learning projects, share those experiences with us!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets to the right people. Plus, it shows us you’re serious about joining our awesome team!
How to prepare for a job interview at QinetiQ
✨Know Your Data
Make sure you brush up on your understanding of data types and machine learning techniques. Familiarise yourself with concepts like supervised and unsupervised learning, as well as any relevant statistical methods. This will help you answer technical questions confidently.
✨Showcase Your Projects
Be ready to discuss any personal or academic projects you've worked on that involve data analysis or programming. Highlight your experience with Python or any cloud computing platforms. This not only shows your skills but also your passion for the field.
✨Prepare for Team Dynamics
Since you'll be working in small project teams, think about examples from your past experiences where you collaborated effectively. Be prepared to discuss how you handle teamwork, communication, and any challenges you've faced while working with others.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions! Inquire about the team’s current projects, the tools they use, or how they approach problem-solving. This shows your genuine interest in the role and helps you understand if it's the right fit for you.