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- Tasks: Design and test machine learning models to enhance sports analytics.
- Company: Dynamic tech company focused on sports analytics and innovation.
- Benefits: Hybrid work, flexible hours, healthcare, and a bonus scheme.
- Why this job: Join a cutting-edge team and revolutionise sports analytics with your skills.
- Qualifications: Strong maths background, Python experience, and innovative problem-solving abilities.
- Other info: Exciting opportunity for career growth in a collaborative environment.
The predicted salary is between 30000 - 50000 £ per year.
A tech company focused on sports analytics is seeking Graduate Machine Learning Researchers to enhance predictive models. Candidates should have a strong mathematical foundation, experience in Python, and a capacity for innovative problem-solving.
The role involves designing and testing machine learning models while participating in the full R&D process. This company offers a hybrid working structure, including remote work, flexible hours, and a range of benefits such as healthcare and a bonus scheme.
Graduate ML Researcher — Sports Analytics & Modelling (Hybrid) employer: Longshot Systems Ltd
Contact Detail:
Longshot Systems Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Graduate ML Researcher — Sports Analytics & Modelling (Hybrid)
✨Tip Number 1
Network like a pro! Reach out to professionals in the sports analytics field on LinkedIn or at industry events. A friendly chat can open doors and give you insights that might just land you that interview.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to sports analytics. This will not only demonstrate your expertise but also your passion for the field.
✨Tip Number 3
Prepare for interviews by brushing up on your problem-solving skills. Be ready to tackle real-world scenarios and explain your thought process. Remember, they want to see how you think, not just what you know!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Graduate ML Researcher — Sports Analytics & Modelling (Hybrid)
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your mathematical foundation and any experience you have with Python. We want to see how your skills align with the role, so don’t hold back!
Be Innovative: This role is all about problem-solving, so share examples of how you've tackled challenges in the past. We love seeing creative approaches, so let your personality shine through!
Tailor Your Application: Don’t just send a generic application. Take the time to tailor your CV and cover letter to reflect the specific requirements of the Graduate ML Researcher position. It shows us you’re genuinely interested!
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, and we can’t wait to see what you bring to the table!
How to prepare for a job interview at Longshot Systems Ltd
✨Brush Up on Your Maths
Since the role requires a strong mathematical foundation, make sure to review key concepts in statistics and linear algebra. Be prepared to discuss how these concepts apply to machine learning models, as this will show your depth of understanding.
✨Show Off Your Python Skills
Practise coding in Python before the interview. You might be asked to solve a problem or explain your previous projects. Having examples ready that showcase your coding skills and how you've used Python in sports analytics will definitely impress them.
✨Think Innovatively
This company values innovative problem-solving, so come prepared with ideas! Think about unique approaches to common challenges in sports analytics. Discussing your thought process and how you tackle problems creatively can set you apart from other candidates.
✨Understand the R&D Process
Familiarise yourself with the full research and development process. Be ready to talk about how you would design and test machine learning models. Showing that you understand the lifecycle of a project will demonstrate your readiness for the role.
