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- Tasks: Design and optimise production-grade knowledge graphs using Neo4j and Python.
- Company: Leading data analytics firm in the UK with a focus on innovation.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Why this job: Make an impact by modelling relationships that drive company processes.
- Qualifications: Experience in data modelling, Python, and graph machine learning.
- Other info: Collaborative environment with exciting projects and career advancement potential.
The predicted salary is between 36000 - 60000 £ per year.
A leading data analytics firm in the UK is looking for a Data Scientist with expertise in Neo4j and Knowledge Graphs. You will design production-grade graph solutions that model relationships across various company processes. The ideal candidate should have strong skills in data modeling, Python, and graph ML.
Responsibilities include:
- Designing ontologies
- Optimizing Neo4j schemas
- Deploying reliable systems
This role emphasizes the importance of strong data models and cross-functional collaboration.
Graph Scientist: Production-Grade Knowledge Graphs in Bristol employer: GM Analytic Software
Contact Detail:
GM Analytic Software Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Graph Scientist: Production-Grade Knowledge Graphs in Bristol
✨Tip Number 1
Network like a pro! Reach out to folks in the data science community, especially those who work with Neo4j and knowledge graphs. Attend meetups or webinars to make connections that could lead to job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to data modelling and graph ML. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your knowledge of ontologies and Neo4j schemas. Be ready to discuss how you've optimised systems in the past and how you approach cross-functional collaboration.
✨Tip Number 4
Don't forget to apply through our website! We have loads of exciting opportunities, and applying directly can sometimes give you an edge. Plus, it’s super easy to keep track of your applications!
We think you need these skills to ace Graph Scientist: Production-Grade Knowledge Graphs in Bristol
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your expertise in Neo4j and Knowledge Graphs right from the start. We want to see how your skills in data modelling and Python can shine through in your application!
Tailor Your Application: Don’t just send a generic CV! We love it when candidates tailor their applications to match the job description. Mention specific experiences that relate to designing ontologies and optimising Neo4j schemas.
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate well-structured applications that make it easy for us to see your qualifications and how you can contribute to our team.
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’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at GM Analytic Software
✨Know Your Graphs
Make sure you brush up on your knowledge of Neo4j and graph ML. Be ready to discuss how you've used these technologies in past projects, and think about specific examples where you designed production-grade graph solutions.
✨Data Modelling Mastery
Prepare to talk about your experience with data modelling. Have a few examples ready that showcase your ability to create strong data models and design ontologies. This will demonstrate your understanding of the role's requirements.
✨Collaboration is Key
Since this role emphasises cross-functional collaboration, think of instances where you've worked with different teams. Be ready to share how you communicated complex ideas effectively and contributed to team success.
✨Optimise Your Schema Knowledge
Familiarise yourself with optimising Neo4j schemas. Be prepared to discuss strategies you've implemented in the past to enhance performance and reliability in your graph systems. This shows you're not just knowledgeable but also practical.
