At a Glance
- Tasks: Design advanced machine learning models to enhance player engagement and value.
- Company: Join PlayStation, a leader in gaming innovation and player experience.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Other info: Collaborative environment with mentorship opportunities and career advancement.
- Why this job: Make a real impact on player experiences through data-driven decisions.
- Qualifications: Experience in machine learning, strong Python and SQL skills, and a relevant academic background.
The predicted salary is between 60000 - 80000 ÂŁ per year.
At PlayStation, Data Science plays a critical role in shaping how we invest in, retain, and delight our global player base. The CLV & Personalisation team focuses on understanding player behaviour and driving more effective engagement across the player lifecycle — from acquisition and onboarding through to retention, monetisation, and long‑term value. As a Senior Data Scientist, you will drive the development of advanced modelling and analytical approaches that influence key commercial and product decisions at scale and are evaluated based on their impact on commercial outcomes. This role combines deep hands‑on modelling with ownership of problem definition, approach, and impact. You will work with a high degree of autonomy across ambiguous problem spaces, shaping how data informs strategy and decision‑making across the business.
What You’ll Do
- Design and deliver advanced machine learning models to drive player value, engagement, and growth across the lifecycle.
- Own ambiguous problem spaces, defining modelling approaches and breaking down complex business challenges into actionable solutions.
- Partner with senior stakeholders to shape strategies, and ensure Data Science drives high‑impact decisions.
- Apply advanced machine learning techniques, including deep learning and sequence modelling, where appropriate to capture complex player behaviours and improve model performance.
- Evaluate and select modelling approaches based on problem context, balancing performance, scalability, and interpretability.
- Work within cross‑functional teams, including engineering, product, and commercial stakeholders, to ensure solutions are scalable, production‑ready, and aligned with long‑term architecture.
- Mentor other data scientists and contribute to best practices across the team.
- Communicate complex modelling approaches, assumptions, and outcomes clearly to senior technical and non-technical audiences.
What We’re Looking For
- Proven industry experience delivering impactful, production‑ready machine learning solutions in areas such as customer value, retention, or personalisation.
- Strong expertise in modern machine learning approaches, including applying techniques such as transformers, embeddings, or sequence‑based models to real‑world behavioural data.
- Ability to critically assess when advanced approaches are warranted versus simpler solutions.
- Experience owning projects end‑to‑end, including problem definition, navigating ambiguity, selecting modelling approaches, and delivering impactful solutions.
- Ability to work with large, complex datasets and translate them into scalable solutions.
- Strong stakeholder management and communication skills, with the ability to influence decisions and communicate results persuasively to both technical and non‑technical audiences.
- Proficiency in Python, SQL, and modern ML tooling (such as Tensorflow, PyTorch, MLlib, feature stores); experience with PySpark or equivalent distributed data processing tools.
- A strong academic background, typically a Master’s or PhD in a technical or quantitative field, specialising in Machine Learning.
- Experience deploying models into production and maintaining business‑critical or consumer‑facing models.
Nice to Have
- Background in gaming, e‑commerce, or subscription‑based businesses.
- Industry experience with experimentation frameworks and causal inference.
- Experience working in agile, fast‑paced environments.
Equal Opportunity Statement
Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.
Senior Data Scientist - CLV & Personalisation in London employer: Sony Interactive Entertainment
Contact Detail:
Sony Interactive Entertainment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist - CLV & Personalisation in London
✨Tip Number 1
Network like a pro! Reach out to folks in the gaming and data science communities. Attend meetups, webinars, or even online forums. You never know who might have the inside scoop on job openings at PlayStation!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to player behaviour or personalisation. This will give you an edge when chatting with potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your communication skills. Be ready to explain complex modelling approaches in simple terms. Remember, you’ll be talking to both technical and non-technical folks!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining the team at PlayStation.
We think you need these skills to ace Senior Data Scientist - CLV & Personalisation in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Senior Data Scientist role. Highlight your experience with machine learning models, especially in customer value and personalisation. We want to see how your skills align with what we’re looking for!
Showcase Your Projects: Include specific examples of projects you've owned from start to finish. Describe the problem, your approach, and the impact of your solutions. This will help us understand your hands-on experience and how you tackle ambiguity.
Communicate Clearly: When writing your cover letter, communicate your ideas clearly and concisely. Remember, you’ll need to explain complex concepts to both technical and non-technical audiences, so show us you can do that right from the start!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and ensure it gets the attention it deserves!
How to prepare for a job interview at Sony Interactive Entertainment
✨Know Your Models Inside Out
As a Senior Data Scientist, you'll need to showcase your expertise in advanced machine learning models. Be prepared to discuss specific projects where you've applied techniques like deep learning or sequence modelling. Highlight how these models impacted player engagement and retention.
✨Master the Art of Communication
You'll be working with both technical and non-technical stakeholders, so it's crucial to communicate complex ideas clearly. Practice explaining your modelling approaches and outcomes in simple terms. This will demonstrate your ability to influence decisions across the business.
✨Embrace Ambiguity
This role involves navigating ambiguous problem spaces, so be ready to discuss how you've tackled similar challenges in the past. Share examples of how you defined problems, selected modelling approaches, and delivered impactful solutions despite uncertainty.
✨Showcase Your Stakeholder Management Skills
Strong stakeholder management is key for this position. Prepare to talk about how you've partnered with senior stakeholders in previous roles. Discuss how you shaped strategies and ensured that data science drove high-impact decisions, making it clear that you can influence at all levels.