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- Tasks: Enhance recommendation systems and analyse user behaviour for actionable insights.
- Company: Leading entertainment publisher with a focus on innovation.
- Benefits: Competitive salary, flexible hours, and opportunities for professional growth.
- Why this job: Join a dynamic team and shape the future of personalised entertainment.
- Qualifications: Proficiency in Python and SQL, strong maths skills, and machine learning knowledge.
- Other info: Collaborative environment with exciting projects and career advancement potential.
The predicted salary is between 30000 - 50000 £ per year.
A leading entertainment publisher is seeking a Data Scientist to enhance recommendation systems and perform behavioral analytics. The ideal candidate must excel in Python and SQL, possess strong mathematical skills, and be familiar with machine learning frameworks.
This role involves integrating predictive models into recommendation strategies and analyzing user behavior to drive actionable insights. Collaboration with cross-functional teams is essential, making strong communication skills a must.
Data Scientist – Personalization & Recommendation Systems employer: Square Enix
Contact Detail:
Square Enix Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist – Personalization & Recommendation Systems
✨Tip Number 1
Network like a pro! Reach out to professionals in the data science field, especially those working with recommendation systems. Use platforms like LinkedIn to connect and engage in conversations about their experiences.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects in Python and SQL, particularly those involving machine learning. This will give potential employers a taste of what you can do and how you approach problem-solving.
✨Tip Number 3
Prepare for interviews by brushing up on your communication skills. Practice explaining complex concepts in simple terms, as you'll need to collaborate with cross-functional teams. Mock interviews can be a great way to build confidence!
✨Tip Number 4
Don't forget to apply through our website! We have loads of opportunities that might just be the perfect fit for you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Data Scientist – Personalization & Recommendation Systems
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your Python and SQL expertise in your application. We want to see how you’ve used these skills in real-world projects, especially in relation to recommendation systems and behavioural analytics.
Math Matters: Don’t shy away from showcasing your mathematical skills! Include any relevant coursework or projects that demonstrate your ability to work with data and algorithms, as this is crucial for the role.
Collaboration is Key: Since this role involves working with cross-functional teams, let us know about your teamwork experiences. Share examples of how you’ve communicated complex ideas effectively to non-technical colleagues.
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 don’t miss out on any important updates during the process!
How to prepare for a job interview at Square Enix
✨Brush Up on Your Technical Skills
Make sure you're comfortable with Python and SQL, as these are crucial for the role. Review key concepts in machine learning frameworks and be ready to discuss how you've applied them in past projects.
✨Showcase Your Analytical Mindset
Prepare examples of how you've used data to drive insights and recommendations. Be ready to explain your thought process when analysing user behaviour and how it influenced your decisions.
✨Communicate Clearly and Effectively
Since collaboration is key, practice explaining complex technical concepts in simple terms. This will help demonstrate your strong communication skills and ability to work with cross-functional teams.
✨Research the Company and Its Audience
Understand the entertainment publisher's target audience and their preferences. This knowledge will allow you to tailor your answers and show how you can enhance their recommendation systems effectively.