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About the Role

As a Data Scientist, you will help measure the impact of PlayStation's products, features, and commercial initiatives through experimentation, causal inference, and applied data science. You will apply robust analytical techniques to solve complex business problems, generate actionable insights, and support evidence-based decision-making across PlayStation, going beyond traditional A/B testing by applying modern causal inference, statistical modeling, and machine learning techniques.

Responsibilities

Apply measurement methodologies across experimentation, causal inference, and advanced analytics to evaluate product and commercial initiatives.
Design and execute robust measurement approaches across randomized experiments, quasi-experimental methods, and observational causal inference where controlled experimentation is impractical.
Apply statistical, machine learning, and AI techniques to solve complex measurement challenges and generate actionable business insights.
Apply advanced data science and machine learning techniques where appropriate to complement experimentation and support complex business decisions.
Contribute to the development and adoption of best practices for experiment design, statistical analysis, and causal measurement.
Partner closely with product managers, engineers, analysts, and business stakeholders to identify high-impact measurement opportunities and ensure product and commercial decisions are supported by rigorous evidence.
Collaborate with engineering teams by providing feedback on experimentation and measurement capabilities to improve tooling and workflows.
Develop reusable analytical solutions, dashboards, and code that improve the quality, consistency, and efficiency of measurement.
Communicate analytical findings and recommendations clearly to technical and non-technical stakeholders.
Stay current with emerging developments in statistics, machine learning, and AI, identifying opportunities to improve measurement capabilities and analytical workflows.
Contribute to a culture of evidence-based decision-making by sharing knowledge, collaborating across teams, and promoting analytical best practices.

Requirements

Master's degree (or equivalent industry experience) in Statistics, Economics, Mathematics, Computer Science, Data Science or another quantitative discipline. PhD preferred.
3+ years of industry experience applying experimentation, causal inference, statistical modelling, machine learning or related analytical techniques to solve business problems.
Good understanding of experimental design, A/B testing, and modern causal inference methods, including quasi-experimental approaches.
Experience applying data science, machine learning, and statistical modelling techniques to analyse complex datasets and generate actionable insights.
Strong proficiency in Python and SQL for analytical workflows.
Experience developing robust analytical solutions using sound statistical principles and software engineering best practices.
Strong stakeholder management and communication skills, with the ability to explain technical concepts clearly to both technical and non-technical audiences.
Curiosity, creativity, and a pragmatic approach to solving ambiguous business problems.
A collaborative mindset with a passion for continuous learning and improving analytical capabilities.
Experience working in gaming, digital products, consumer technology, e-commerce or subscription businesses.
Familiarity with modern data engineering and analytics tooling such as Databricks, Snowflake, Git and Airflow.
Experience with modern business intelligence and data visualisation tools such as Tableau, Domo or Power BI.
Experience applying AI tools, including large language models (LLMs), to improve data science workflows and analytical productivity.
Experience developing reusable analytical frameworks, libraries or internal tooling.
Experience presenting analytical findings and recommendations to cross-functional stakeholders.

Skills

Experimentation
Causal Inference
Applied Data Science
Statistical Modelling
Machine Learning
A/B Testing
Quasi-experimental methods
Observational causal inference
Python
SQL
Data Engineering
Analytics Tooling
Databricks
Snowflake
Git
Airflow
Business Intelligence
Data Visualization
Tableau
Domo
Power BI
AI Tools
Large Language Models (LLMs)

Location

Hybrid

Work Type

Hybrid Working (FlexModes)

Experience Level

3+ years of industry experience

Education Level

Master's degree (or equivalent industry experience)
PhD preferred

Benefits

Discretionary bonus opportunity
Hybrid Working (FlexModes)
Private Medical Insurance
Dental Scheme
25 days annual leave
On-site gym
Subsidised café
Complimentary soft drinks
On-site bar
Secure cycle storage and shower facilities

About the Company

Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand.
As a subsidiary of Sony Group Corporation, SIE is part of a proud legacy of innovation and excellence.
SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world.
Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity.

Equal Opportunity

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.