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- Tasks: Collaborate on data insights, train machine learning models, and enhance software applications.
- Company: Quest Global, a leader in engineering services with a mission to make a positive impact.
- Benefits: Gain hands-on experience, work in a supportive culture, and develop your skills in a dynamic environment.
- Why this job: Join a team that values innovation and empowers you to solve real-world problems.
- Qualifications: Familiarity with data science methods and programming skills in Python or JavaScript.
- Other info: Entry-level position with opportunities for growth and collaboration.
The predicted salary is between 28800 - 48000 £ per year.
At Quest Global, it's not just what we do but how and why we do it that makes us different. With over 25 years as an engineering services provider, we believe in the power of doing things differently to make the impossible possible. Our people are driven by the desire to make the world a better place—to make a positive difference that contributes to a brighter future. We bring together technologies and industries, alongside the contributions of diverse individuals who are empowered by an intentional workplace culture, to solve problems better and faster.
Key Responsibilities
- Collaborate with data and subject matter experts and customer teams to seek, understand, validate, interpret, and correctly use data and business insights.
- Train, validate and deploy machine learning pipelines.
- Test, troubleshoot, and enhance customer software applications with and on behalf of customers.
- Identify application performance bottlenecks and implement optimizations.
- Write application specifications, tutorials and documentation.
Qualifications
- Experience in data science methods, including statistical analysis, machine learning, and data visualization.
- Experience identifying and resolving operational or data-related issues using documented procedures and prior case studies.
- Experience making small-scale system and process improvements to enhance functionality and efficiency.
Preferred Qualifications
- Applied Machine Learning experience (regression, classification, supervised, and unsupervised learning) with a strong mathematical foundation in linear algebra, calculus, probability, and statistics.
- Experience in time-series data analysis, including cleansing and normalization, and experience with scalable Machine Learning (MapReduce, streaming).
- Software development expertise in Python and JavaScript, with experience using Git or other version control systems.
- Ability to drive projects independently and collaboratively, demonstrating a strong work ethic and commitment to organizational goals.
Seniority Level: Entry level
Employment Type: Temporary
Job Function: Information Technology
Data Science Engineer in London employer: Quest Global
Contact Detail:
Quest Global Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Science Engineer in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data science projects, especially those involving machine learning and data visualisation. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on common data science questions and practical tests. Practice explaining your thought process clearly, as communication is key in collaborative environments.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Data Science Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Science Engineer role. Highlight relevant experience in data science methods, machine learning, and any software development skills you have. We want to see how your background aligns with what we're looking for!
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 you can contribute to our mission at Quest Global. Be genuine and let your personality come through—this is your moment to connect with us.
Showcase Your Projects: If you've worked on any projects related to data science or machine learning, make sure to mention them! Whether it's a personal project or something from your studies, we love seeing practical applications of your skills. Include links if possible!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us that you're serious about joining our team at Quest Global!
How to prepare for a job interview at Quest Global
✨Know Your Data Science Basics
Make sure you brush up on your data science methods, especially statistical analysis and machine learning. Be ready to discuss how you've applied these techniques in past projects, as this will show your understanding and practical experience.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've identified and resolved operational or data-related issues. Use the STAR method (Situation, Task, Action, Result) to structure your answers, making it easy for the interviewer to see your thought process.
✨Familiarise Yourself with Machine Learning Pipelines
Since the role involves training and deploying machine learning pipelines, be prepared to explain your experience with this. Discuss any specific projects where you’ve built or optimised these pipelines, highlighting your technical skills in Python and JavaScript.
✨Demonstrate Your Collaborative Spirit
Quest Global values teamwork, so think of instances where you've collaborated with others to achieve a goal. Highlight your ability to work independently as well, but don’t forget to emphasise how you can contribute to a team environment.
