At a Glance
- Tasks: Dive into data science, AI, and digital product development with hands-on experience.
- Company: Global tech group at the forefront of engineering and digital innovation.
- Benefits: Hybrid work model, international travel, and a vibrant team culture.
- Other info: Join a collaborative environment with opportunities for international exposure.
- Why this job: Make a real impact in AI and data-driven projects while learning and growing.
- Qualifications: Degree in a scientific or technical field; Python knowledge is a plus.
The predicted salary is between 28000 - 35000 £ per year.
Our client is a global technology group operating at the intersection of engineering, science, and digital innovation. They are investing heavily in AI, data, and connected technologies to transform both their products and operations.
This is a hands-on, early-career role designed for a technically curious graduate who wants to build real-world experience in data science, AI, and digital product development. You will sit within a central digital team supporting multiple business units on AI and data-driven initiatives.
This is not a finance-driven analytics role. The focus is on scientific, engineering, and operational data, including time-series and image data, with exposure to machine learning experimentation and modern AI tooling, including GenAI.
What You’ll Be Doing- Data Preparation and Engineering
- Collect, clean, and validate data from sensors, internal systems, APIs, and files.
- Build structured, reproducible datasets for analysis and modelling.
- Identify and resolve data quality and integrity issues.
- Exploratory Analysis and Insight Generation
- Perform exploratory data analysis to identify trends, anomalies, and patterns.
- Translate findings into clear, structured insights for stakeholders.
- Machine Learning and AI Support
- Support development and testing of machine learning pipelines.
- Work with time-series, tabular, and image datasets.
- Assist with experimentation, model comparison, and evaluation.
- Contribute to early-stage work in areas such as generative AI and language models.
- Data Visualisation and Communication
- Build dashboards, charts, and reports using Python or BI tools.
- Present outputs clearly to technical and non-technical audiences.
- Technology Research and Evaluation
- Assess AI tools and platforms, documenting strengths, limitations, and risks.
- Support evaluation of both internal and third-party solutions.
- Governance and Best Practice
- Maintain clear documentation and reproducible workflows.
- Support responsible AI practices and data governance standards.
- Degree in a scientific or technical discipline such as Physics, Chemistry, Biology, Engineering, Mathematics, or Data Science.
- If from a Computer Science background, proven exposure to scientific or experimental data.
- Working knowledge of Python (pandas, NumPy).
- Understanding of core machine learning concepts.
- Strong analytical thinking and attention to detail.
- Ability to communicate findings clearly.
- Experience with time-series or image data (academic or project-based).
- Exposure to machine learning workflows or experimentation.
- Experience with data visualisation tools.
- Familiarity with Git.
- Interest in generative AI and emerging AI technologies.
- A graduate or early-career candidate within 0–2 years.
- Strong scientific or engineering mindset, not finance-focused.
- Highly organised with strong documentation habits.
- Logical thinker who uses AI tools appropriately, not blindly.
- Curious, proactive, and comfortable learning through experimentation.
- Work across a wide range of AI, IoT, and digital product initiatives.
- Exposure to modern AI tooling and real-world applications.
- International project exposure across Europe and the US.
- Strong team culture with regular social events and collaboration.
- Hybrid model combining London office, remote work, and travel.
- Project-based international travel required.
Someone early in their career who wants to apply data and AI in real-world scientific and engineering contexts, rather than sitting in a purely reporting or finance-driven analytics role.
AI Data Analyst (Early-Career) - Time-Series & Images employer: Anson McCade
Our client is an exceptional employer, offering a dynamic work environment that fosters innovation and collaboration in the fields of AI and data science. With a strong emphasis on employee growth, you will have the opportunity to engage in international projects and gain hands-on experience with cutting-edge technologies, all while enjoying a supportive team culture that values curiosity and experimentation. The hybrid working model allows for flexibility, making it an ideal place for early-career professionals eager to make a meaningful impact in scientific and engineering contexts.
StudySmarter Expert Advice🤫
We think this is how you could land AI Data Analyst (Early-Career) - Time-Series & Images
✨Tip Number 1
Network like a pro! Reach out to professionals in the AI and data science fields on LinkedIn. Join relevant groups, attend webinars, and don’t be shy about asking for informational interviews. You never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving time-series or image data. Use platforms like GitHub to share your code and visualisations. This will give potential employers a taste of what you can do!
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss machine learning concepts and your experience with Python. Practice explaining your projects clearly, as you’ll need to communicate effectively with both technical and non-technical audiences.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for curious minds eager to dive into AI and data. Keep an eye on our listings and make sure your application stands out by tailoring it to the role.
We think you need these skills to ace AI Data Analyst (Early-Career) - Time-Series & Images
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the AI Data Analyst role. Highlight any relevant projects or coursework, especially those involving time-series or image data, to show us you're a great fit!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and data science, and how your background makes you the perfect candidate for this hands-on role. Keep it engaging and personal!
Showcase Your Technical Skills:Don’t forget to mention your proficiency in Python and any experience with data visualisation tools. We want to see how you can contribute to our projects right from the start, so be specific about your technical abilities!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates during the process!
How to prepare for a job interview at Anson McCade
✨Know Your Data
Make sure you brush up on your knowledge of data preparation and engineering. Be ready to discuss how you would collect, clean, and validate data from various sources. Familiarise yourself with time-series and image data, as well as the tools like Python that you'll be using.
✨Show Your Curiosity
This role is all about being technically curious! Prepare to share examples of how you've explored data in the past or any projects where you've experimented with machine learning. Highlight your eagerness to learn and adapt, especially in areas like generative AI.
✨Communicate Clearly
You’ll need to present your findings to both technical and non-technical audiences. Practice explaining complex concepts in simple terms. Think about how you can use visualisation tools to make your insights more accessible and engaging.
✨Demonstrate Organisational Skills
Being highly organised is key for this role. Bring examples of how you've maintained documentation and reproducible workflows in your previous work or studies. Show that you understand the importance of governance and best practices in data science.
