Data Scientist - Experimentation & Measurement

Why Play Station?

Play Station isn’t just the Best Place to Play — it’s also the Best Place to Work. Today, we’re recognized as a global leader in entertainment producing The Play Station family of products and services including Play Station®5, Play Station®4, Play Station®VR, Play Station®Plus, acclaimed Play Station software titles from Play Station Studios, and more.

Play Station also strives to create an inclusive environment that empowers employees and embraces diversity. We welcome and encourage everyone who has a passion and curiosity for innovation, technology, and play to explore our open positions and join our growing global team.

The Play Station brand falls under Sony Interactive Entertainment, a wholly‑owned subsidiary of Sony Group Corporation.

Our Data Science Analytics and Enablement (DSAE) team inspires Play Station to make impactful, customer centric decisions through seamless integration of data.

Currently there are over 100 people in the global DSAE team, including data science, data governance and analytics professionals. We work closely with engineering and product management teams to deliver data products, insight, predictive analytics, and data visualisation.

DSAE is looking to recruit talented, highly driven individuals who have excelled in previous roles and are looking for a new challenge in a dynamic and rewarding environment.

As a Data Scientist within our experimentation and measurement work, you will support how Play Station tests, measures, and learns across key products and initiatives. You will work alongside senior data scientists and cross‑functional partners to deliver robust experimental analysis and build experience in causal and econometric techniques used in real‑world settings.

This role is based in London with hybrid working flexibility.

• Support the design, execution, and analysis of experiments (e.g. A/B tests), including metric definition, validation, and interpretation
• Apply statistical testing techniques to evaluate experimental outcomes and quantify impact
• Assist with causal analysis where full randomisation is not feasible, using established approaches such as pre/post analysis, matched cohorts, or difference‑in‑differences, under guidance
• Contribute to exploratory econometric‑style analyses to better understand drivers of player behaviour and commercial outcomes
• Partner with engineering, analytics, and product teams to ensure accurate experiment setup and clean data capture
• Monitor live experiments and analyses, flagging data quality issues or unexpected results
• Produce clear analysis outputs, visualisations, and summaries that explain results, assumptions, and limitations
• Contribute to experiment readouts and stakeholder updates with support from senior team members
• Follow established experimentation standards and contribute to documentation, tooling, or reusable analysis patterns
• Continue to build skills in experimentation, causal inference, and applied analytics through hands‑on work and mentorship

• Early career experience in data science, analytics, or a related quantitative role
• Degree (or equivalent experience) in a quantitative field such as Statistics, Economics, Applied Mathematics, Computer Science, or similar
• Proven foundation in statistics, including hypothesis testing and basic experimental analysis
• Familiarity with A/B testing concepts and an interest in causal inference and econometric approaches
• Proficiency in SQL and Python (or R) for data querying, preparation, and analysis
• Ability to clearly communicate analytical findings to non‑technical partners
• Strong attention to detail and commitment to analytical rigor and data quality
• Collaborative, proactive attitude with comfort working across product, engineering, and analytics teams
• Curiosity and motivation to develop deeper expertise in experimentation, causal analysis, and measurement over time
• Exposure to digital products, e‑commerce, gaming, or customer lifecycle analytics is a plus, but not required
• Knowledge of machine learning approaches is a plus, but not required

• Discretionary bonus…