
Data Science Manager – Experimentation: Innovation & Research
- London
- Permanent
- Full-time
- Lead a team of data scientists focused on experimentation and causal inference; provide technical direction, career development, and mentorship.
- Drive innovation in experimentation research by developing and overseeing new methodologies and frameworks that improve the quality, speed, and scalability of experiments.
- Guide the advancement of experimentation infrastructure and tooling, incorporating statistical and machine learning methods to refine analysis capabilities.
- Partner with product managers, game studios, and business leaders to identify high-impact experimentation opportunities and ensure alignment with PlayStation's strategic goals.
- Act as a thought leader in experimentation and causal inference, evangelizing best practices and fostering learning across teams.
- Contribute directly to research and prototyping of novel experimentation techniques that address complex real-world constraints, such as user behavior variability and data limitations.
- Champion the growth of a data-driven culture by advocating for experimentation standards, ethical practices, and reproducibility.
- Represent the team's insights, innovations, and impact across the broader data science and product communities within PlayStation.
- Stay abreast of emerging developments in experimentation, causal inference, and applied machine learning to continuously evolve our capabilities.
- Master's Degree or equivalent experience in Applied Math, Economics, Statistics, Computer Science, or related field. Ph.D. or equivalent experience preferred.
- Strong familiarity with the gaming industry and contemporary gaming experiences.
- 6+ years of experience in data science, including hands-on work in experimentation, with at least 2+ years in a formal people management or technical leadership role.
- Proven track record of leading experimentation innovation and scaling frameworks within a dynamic business environment.
- Proficiency in SQL and statistical programming languages (e.g., R or Python), especially for causal inference, experimental analysis, and scalable modeling.
- Expertise in causal inference techniques and designing both randomized and quasi-experiments.
- Demonstrated ability to collaborate cross-functionally and influence data strategies that inform business and product decisions.
- Excellent communication and storytelling skills, especially in conveying complex concepts to non-technical stakeholders.
- Experience working with modern data engineering and visualization tools (e.g., Airflow, Git, Tableau, MicroStrategy).
- A strong sense of ownership and an inclusive leadership style that encourages collaboration and innovation.