Tennis Performance Analytics Placement — Mentored MSc in London

Tennis Performance Analytics Placement — Mentored MSc in London

London Placement 20000 - 30000 £ / year (est.) No working from home possible
Comunidadlift

At a Glance

  • Tasks: Analyse tennis performance data and support coaching with insights.
  • Company: Comunidadlift, a dynamic organisation in the sports analytics field.
  • Benefits: Hands-on experience, mentorship from experts, and skill development.
  • Other info: Exciting opportunity for growth in a fast-paced environment.
  • Why this job: Gain valuable insights into sports analytics while making a real impact.
  • Qualifications: Enrolled in an MSc programme with strong analytical skills.

The predicted salary is between 20000 - 30000 £ per year.

Comunidadlift is seeking a Performance Analysis intern in Greater London. This position offers immersive learning with hands-on experience in performance analysis using Microsoft Excel and Power BI. The intern will develop skills under the mentorship of experts in the field.

Responsibilities include:

  • Delivering statistical intelligence for coaching
  • Direct player support
  • Maintaining performance analysis reports

Ideal candidates will be enrolled in the MSc program and possess strong analytical skills.

Tennis Performance Analytics Placement — Mentored MSc in London employer: Comunidadlift

Comunidadlift is an exceptional employer that fosters a dynamic work culture in Greater London, where interns are empowered to grow through hands-on experience and mentorship from industry experts. With a focus on professional development, employees benefit from immersive learning opportunities and the chance to contribute meaningfully to performance analysis in sports. Join us to enhance your analytical skills while being part of a supportive team dedicated to excellence in coaching and player support.

Comunidadlift

Contact Details:

Comunidadlift Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Tennis Performance Analytics Placement — Mentored MSc in London

Get Involved in Data Communities

Join local data science meetups or online communities like Kaggle, where you can showcase your skills through competitions and collaborate with others. Being part of these groups not only boosts your visibility but also opens up potential placement opportunities!

Leverage University Career Services

Don’t forget to check in with your university’s career services! They often have exclusive placement opportunities, workshops, and networking events tailored for data science students that can help you land that sweet gig at Comunidadlift.

Show Off Your Projects

Create a public portfolio or blog where you can share your data science projects. This not only demonstrates your skills but also gives potential employers like Comunidadlift a taste of what you can do! Plus, it’s a great conversation starter in interviews.

Connect with Professionals on LinkedIn

Reach out to data science professionals on LinkedIn and ask for informational interviews. Get to know their journeys and share your interest in placements. This approach makes you memorable and could open doors to opportunities at places like Comunidadlift.

We think you need these skills to ace Tennis Performance Analytics Placement — Mentored MSc in London

Performance Analysis
Microsoft Excel
Power BI
Statistical Intelligence
Analytical Skills
Coaching Support
Report Maintenance

Some tips for your application 🫡

Show off Your Data Skills:As a budding data scientist, we want to see your hands-on experience. Make sure your CV highlights any relevant projects you've worked on, especially those involving data analysis or machine learning. Include any programming languages you're proficient in, like Python or R, and don't forget to mention tools like TensorFlow or SQL that you’ve used!

Craft a Passionate Cover Letter:For a placement, your enthusiasm and eagerness to learn mean loads to us! In your cover letter, express what excites you about data science and how it aligns with your aspirations. Share specific examples of what you've learned so far – whether it's courses, personal projects, or even data challenges you've tackled!

Make Your Projects Pop:We love seeing practical examples of your work, so if you have a portfolio, now's the time to shine! Include detailed descriptions of your projects, the data sources you used, the challenges you faced, and what you achieved. If possible, share links to your GitHub or a personal website showcasing your work – it adds that extra touch!

Demonstrate Statistical Knowledge:Data science isn't just about coding; it's also about understanding the stats behind the algorithms. Be sure to highlight any coursework or relevant certifications you have in statistics or data analysis on your CV. It’ll show us you’ve got the theoretical background to complement your practical skills!

How to prepare for a job interview at Comunidadlift

Master Your Data Tools

For a data science placement, it's crucial to get comfy with tools like Python, R, or SQL. Brush up on libraries like Pandas and NumPy, as you might be thrown some technical questions during the interview that require real-time coding or problem-solving.

Showcase Your Projects

Bring along a portfolio of your data projects, especially if they're relevant to Comunidadlift. Whether it's a Kaggle competition or a personal project, showing off your practical work can really set you apart and demonstrate your hands-on experience.

Prepare for Case Studies

Interviews in data science often include case study questions. Be ready to walk through your thought process on how to approach a data problem, what analysis you'd do, and how you'd interpret the results. This shows your analytical thinking and understanding of data.

Express Your Enthusiasm

Since it's a placement, they're looking for potential and enthusiasm just as much as skills. Be sure to express why you're passionate about data science, how you're eager to learn, and why you want to join Comunidadlift specifically. Let your motivation shine through!