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- Tasks: Use data science to solve real-world challenges in the mining sector.
- Company: Join a forward-thinking team at the intersection of geology and data science.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Make a real impact by transforming geological data into actionable insights.
- Qualifications: Background in data science or geology with strong analytical skills.
- Other info: Collaborative environment with exciting projects and career advancement potential.
The predicted salary is between 36000 - 60000 £ per year.
Job Description
DATA SCIENCE – GEOLOGY
We are seeking a talented and motivated individual with a background in either data science or geology to join our team in a cross-disciplinary role. This position offers a unique opportunity to apply advanced analytics and spatial modelling techniques to solve real-world challenges in the mining sector.
You will collaborate with development teams, product owners, and subject matter experts to transform geological and mining data into actionable insights, contributing to the development of cutting-edge technology that enhances subsurface understanding and decision-making.
Key Responsibilities
- Collaborate with cross-functional teams to integrate data science capabilities into software products.
- Analyse and interpret complex geological and mining datasets to generate meaningful insights.
- Contribute to innovative projects such as:
- Inferring rock properties from drilling data
- Merging primary and response rock properties using machine learning
- Applying self-learning models to geological data
- Build and deploy data-driven solutions that support geological decision-making.
- Apply spatial data analysis techniques to improve subsurface data interpretation.
Candidate Profile
Essential Skills and Experience:
- Background in data science with a focus on geostatistics/geology, or geology with strong data science experience
- Strong analytical and problem-solving skills
- Experience with data analysis, modelling, and visualisation tools
- Understanding of spatial and geospatial data concepts
- Ability to communicate technical insights to non-technical stakeholders
- Collaborative mindset and ability to work effectively in a team environment
Preferred (Bonus) Skills:
- Familiarity with machine learning and self-learning algorithms
- Experience working with geological and mining datasets
- Understanding of software development tools and workflows
- Exposure to 3D data modelling or geological simulation tools
Data scientists employer: Opus Recruitment Solutions
Contact Detail:
Opus Recruitment Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data scientists
✨Tip Number 1
Network like a pro! Reach out to professionals in the geology and data science fields on LinkedIn. Join relevant groups, attend webinars, and don’t be shy about asking for informational interviews. We all know that sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to data analysis and geostatistics. Use platforms like GitHub to share your code and visualisations. This way, potential employers can see your work in action, and it gives you a great talking point during interviews.
✨Tip Number 3
Prepare for those interviews! Research common questions related to data science and geology, and practice your answers. We recommend using the STAR method (Situation, Task, Action, Result) to structure your responses. This will help you articulate your experiences clearly and confidently.
✨Tip Number 4
Don’t forget to apply through our website! We regularly update our job listings, and applying directly can sometimes give you an edge. Plus, it shows your enthusiasm for joining our team. Keep an eye out for roles that match your skills and interests!
We think you need these skills to ace Data scientists
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data science or geology, especially any relevant projects. We want to see how your skills align with the role, so don’t be shy about showcasing your analytical prowess and problem-solving abilities!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about this role and how your background makes you a perfect fit. We love seeing enthusiasm and a clear connection between your experience and our needs.
Showcase Your Projects: If you've worked on any cool projects involving data analysis, modelling, or visualisation, make sure to mention them! We’re keen to see how you’ve applied your skills in real-world scenarios, especially in geology or mining contexts.
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 shows you’re proactive and really interested in joining our team!
How to prepare for a job interview at Opus Recruitment Solutions
✨Know Your Data Science and Geology
Make sure you brush up on both data science and geology concepts. Be ready to discuss how you've applied analytical techniques to geological datasets in the past. This will show your potential employer that you can bridge the gap between these two fields effectively.
✨Showcase Your Problem-Solving Skills
Prepare examples of complex problems you've solved using data analysis or modelling. Think about specific projects where you used spatial data analysis or machine learning. This will help demonstrate your analytical mindset and ability to generate actionable insights.
✨Communicate Clearly with Non-Technical Stakeholders
Practice explaining technical concepts in simple terms. You might be asked to present your findings to team members who aren't data experts, so being able to communicate effectively is key. Use relatable analogies or visuals to make your points clearer.
✨Collaborate and Engage
Since this role involves working with cross-functional teams, be prepared to discuss your collaborative experiences. Share examples of how you've worked with others to integrate data science into projects, and highlight your ability to adapt and contribute to a team environment.
