PlayStation is looking to safeguard its expansive digital commerce ecosystem from fraud risks and optimize payment security through data-driven solutions.
Requirements
- Strong expertise in causal inference, anomaly detection, supervised/unsupervised learning, and real-time ML
- Proficiency in SQL and Python (Pandas, NumPy, Scikit-learn)
- Experience designing and analyzing A/B tests and applying causal inference techniques
- Experience with graph-based fraud detection, deep learning for risk, or adversarial ML techniques (bonus)
- 5+ years of experience in data science, ML, and fraud/risk analytics, preferably in fintech, payments, or e-commerce
- Master’s or Ph.D. in a quantitative field (Computer Science, Data Science, Statistics, etc.)
Responsibilities
- Develop and refine fraud and risk modeling strategies
- Contribute to innovation in ML for fraud detection, real-time inference, and anomaly detection
- Design and implement A/B tests to evaluate the impact of new product features and initiatives
- Apply causal inference techniques to assess the effectiveness of fraud detection, risk management, and user retention efforts
- Analyze and optimize key business metrics related to revenue, conversion, and engagement
- Enhance experimentation frameworks to ensure models and risk mitigation strategies are rigorously validated
- Collaborate with collaborators across finance, risk, and product teams to drive data-informed decision-making
Other
- Master’s or Ph.D. in a quantitative field (Computer Science, Data Science, Statistics, etc.)
- 5+ years of experience in data science, ML, and fraud/risk analytics, preferably in fintech, payments, or e-commerce
- Collaborate with collaborators across finance, risk, and product teams to drive data-informed decision-making
- PlayStation fan (bonus)
- Equal Opportunity Employer: 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.
