Senior Data Scientist

PlayStation Global•Los Angeles, CA

3d•$167,200 - $250,800

About The Position

At PlayStation, Data Science plays a critical role in shaping how we invest in, retain, and delight our global player base. Our Player Value Science team turns understanding of value mechanics into incrementality: we explain what drives value, where it is created or missed, and what to change, so growth can be created and protected. As a Senior Data Scientist, you will drive the development of advanced modelling and analytical approaches that influence high-stakes commercial and portfolio decisions at scale. Working closely with stakeholders across Finance, Strategy and Platform, you will work on complex and often ambiguous problems with a high degree of autonomy to shape how data informs strategy and investment decisions through a deeper understanding of player behaviour, product usage, and long-term value. This role combines deep technical expertise with strong commercial judgement. You will own the problem definition and approach, and success will be measured not only by the quality of your work, but by the adoption and impact on business outcomes.

Requirements

Proven industry experience delivering impactful, decision-grade analytical and modelling solutions in areas such as customer value, incrementality, forecasting, or investment decisioning.
Experience with optimization or operations research approaches (for example resource allocation, scheduling, or constrained planning problems), or a strong quantitative foundation to pick these up quickly.
Strong grounding in causal inference and impact measurement, with the judgement to critically assess assumptions, recognise when correlation is not enough and select approaches that are proportionate to the problem being solved.
Experience owning projects end-to-end, from problem definition and selecting methods through to delivery and business adoption.
Ability to work with large, complex datasets and translate them into scalable, trustworthy solutions.
Exceptional stakeholder management and communication skills, with a track record of influencing commercial or finance partners and communicating results persuasively to both technical and non-technical audiences.
Proficiency in Python and SQL, and comfort across the modern analytical and ML tooling ecosystem; experience with PySpark or equivalent distributed data processing tools.
A strong quantitative background, typically a Master's or PhD in operations research, machine learning, statistics, economics, or a related field.

Nice To Haves

Background in gaming, e-commerce, or subscription-based businesses.
Experience partnering with Finance or commercial functions on portfolio, investment, or impact-attribution problems.
Industry experience with experimentation frameworks and causal inference.
Expertise in modern machine learning approaches, including applying techniques such as transformers, embeddings, or sequence-based models to real-world behavioural data.
Experience working in agile, fast-paced environments

Responsibilities

Design and deliver advanced modelling approaches that quantify player value, incremental impact, and long-term platform value, applying techniques such as causal inference, forecasting, optimization and machine learning where appropriate.
Measure the incremental effect of launches, investments, and external market events, isolating true impact from what would have happened anyway, and translating that into an actionable understanding of where value is created.
Build optimization and scenario models that inform long-range planning and investment decisions under real-world constraints.
Own ambiguous problem spaces, and break down complex business challenges into tractable, measurable solutions.
Partner closely with cross-functional teams across Finance, Strategy and Platform to ensure value-driving products are robust, trusted and adopted, helping stakeholders understand how to recognise and attribute impact.
Evaluate and select modelling approaches based on problem context, balancing rigour, performance, scalability, interpretability, and business impact.
Mentor other data scientists and contribute to best practices across the team.
Communicate complex modelling approaches, assumptions and outcomes clearly to technical and non-technical audiences, including Finance leadership.