Playstation

San Mateo, CA
5,001-10,000 employees
We've defined gaming for generations. Our collective of studios and teams span the globe, advancing gaming and the positive impact it has on people’s lives.

Data Scientist

Data Scientist

This job is no longer open

Data Scientist – Global Payments & Fraud Management (GSS)
 
Interested in working on strategic initiatives that allow for creativity and room for growth?  Do you have strong passion for using data and cutting-edge technologies to drive the best business decisions?  If so, read on…
Sony Interactive Entertainment’s (SIE) Global Fraud Management and Global Payment Teams are the guardians of both customer trust and purchase success for PlayStation and the PlayStation® Network (PSN).  We provide innovative solutions to support every element of the network, various platform services, customer service teams, a diverse developer community, and more. 
SIE has just launched a next generation risk platform and machine learning framework that support the global, fast growing PlayStation® Network customer base, world class PlayStation® consoles, hand-held devices, PlayStation® TV, PlayStation® Music, PlayStation® Video and PlayStation® Now.  In doing so, the ability to extract information from data and drive action that help achieve business goals is essential.  GPFM’s Data Science team is responsible for evaluating online user activity data for potential fraud across this new platform.  Our job is to provide quantitative assessments of all customers and identify those individuals who pose the most risk to our businesses. We are currently looking for a high-caliber individual to join our team to demonstrate the benefits of being data driven for the Global Fraud Management, Global Payments and Account Security Teams.
Responsibilities: 
1.    Utilize various approaches to maximize value from predictive analytics solutions.  Optimize use of developed Machine-Learning algorithms in conjunction with other aspects of decision logic (rules, other models, etc.) 
2.    Demonstrate understanding of predictive modeling methodology that detect fraud and other ecommerce risks. Provide feedback to modelers on new features, algos, strategy, and modeling ideas. 
3.    Conduct robust statistical analysis to big data using hypothesis testing, design of experiment, inferential statistics, and more.
4.    Improve decision performance by analyzing misclassification cases, hit rates, and customer feedback. Organize, document and simplify rule assessment.
5.    Develop deep understanding of business challenges and recommend data features and functionality that can contribute to identifying risk and possible fraud. 
6.    Assist the Software Development and Peer team members in testing and deploying decision changes (rules, models, etc.) on the newly created platform

Requirements: 
1.    Master’s degree in Statistics, Operations Research, Machine Learning or other related fields and a minimum of 5 years’ related work experience.
2.    2+ years working with statistical modeling techniques (Regression, CHAID, Neural Networks, Cluster Analysis, RFs, XGBs, etc.).   
3.    2+ years’ experience doing hands-on statistical analysis with some understanding of a statistical programming package such as SAS/SPSS/R/Python/Java/other is required.

Preferred Skills:
1.    Proven recent, experience working with optimization, statistical analysis, machine learning or text classification algorithms. 
2.    Strong understanding of predictive modeling, data manipulation, data value assessment.  Creation and understanding of macros for automating above mentioned processes is strongly preferred. 
3.    The candidate should demonstrate high energy/creativity, a passion for analyzing highly complex data sets, strong communication and project management skills, an entrepreneurial spirit, a relentless customer-focus, a practical understanding of quantitative methods, and superb attention to detail.

This job is no longer open
Logos/outerjoin logo full

Outer Join is the premier job board for remote jobs in data science, analytics, and engineering.