Graduate Knowledge Engineer β€” AI-Ready Data & Graphs (Hybrid)

Graduate Knowledge Engineer β€” AI-Ready Data & Graphs (Hybrid)

Full-Time 51102 - 62458 Β£ / year (est.) Home office (partial)
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At a Glance

  • Tasks: Design advanced knowledge systems for data handling and AI readiness.
  • Company: Craxel, a forward-thinking tech company based in Belfast.
  • Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
  • Other info: Collaborate with teams across the UK and US in a vibrant work environment.
  • Why this job: Join a dynamic team and shape the future of AI with innovative technology.
  • Qualifications: Recent graduates with a passion for data and technology.

The predicted salary is between 51102 - 62458 Β£ per year.

Craxel in Belfast is seeking a Graduate Knowledge Engineer to join our UK team in a hybrid role, office-based three days a week.

You will work with clients and partners to design advanced knowledge systems for data handling and AI readiness.

The role focuses on data contextualisation, graph representations, and collaboration with UK/US teams to analyse complex datasets for decision making, leveraging Craxel's Black Forest technology.

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Graduate Knowledge Engineer β€” AI-Ready Data & Graphs (Hybrid) employer: Craxel

At Craxel, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. Our Graduate Knowledge Engineers benefit from a competitive salary, comprehensive health insurance, and generous holiday allowances, all while working in a hybrid environment in the vibrant city of Belfast. With continuous learning opportunities and the chance to engage with cutting-edge technology, employees are empowered to grow their careers and make a meaningful impact in the field of data analytics and AI.

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Contact Details:

Craxel Recruitment Team

StudySmarter Expert Advice🀫

We think this is how you could land Graduate Knowledge Engineer β€” AI-Ready Data & Graphs (Hybrid)

✨Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Craxel!

✨Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Graduate Knowledge Engineer β€” AI-Ready Data & Graphs (Hybrid) at Craxel.

✨Leverage Professional Networks

Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Craxel.

✨Apply Directly through Our Website

When you find a suitable opening like Graduate Knowledge Engineer β€” AI-Ready Data & Graphs (Hybrid) at Craxel, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace Graduate Knowledge Engineer β€” AI-Ready Data & Graphs (Hybrid)

Python
SQL
Problem-Solving Skills
Communication Skills
Data Engineering
Data Pipeline Development
API Integration

Some tips for your application 🫑

Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!

Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!

Craft a Tailored Cover Letter:For a full-time role at Craxel, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.

Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Craxel. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!

How to prepare for a job interview at Craxel

✨Brush Up on Your Statistics

For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!

✨Showcase Your Projects

Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!

✨Get Comfortable with Python and R

Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Craxel!

✨Prepare for Case Studies

Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.