Sr. Data ScientistAustin, Texas
San Jose, California Requisition Number R0032403 Subsidiary eBay
At eBay, you will be part of a purpose driven community dedicated to creating a bold and versatile work environment. In eBay Payments, you will be an integral member of a growing organization that inspires passion, courage and inventiveness - creating the future of global commerce and making an important, positive impact on millions of eBay sellers and shoppers around the world. If you are looking for a special place to take your Payments career to the next level, we want to talk with you!
Risk Management is at the core of Payments done well – and we are hiring curious, driven, and courageous experts to transform our business unit to enable eBay's next generation Payments strategy. Our focus is to ensure the integrity of our marketplace for buyers and sellers who transact with us every single day. The scope of our charter includes Risk Management Strategy, Policy, Decision Sciences, and Policy Operations.
We are looking for a highly talented and self-motivated data scientist to join our Decision Science team. Decision Science contains both data scientists and software engineers responsible for creating and implementing state of the art machine learning algorithms for fraud detection and risk assessment in support of Risk Management. The primary responsibility of this role is to assist in algorithm development inside of a high throughput, low latency, big data environment.
Primary Job Responsibilities
The senior data scientist will support the risk department, leveraging big data technologies to aggregate and structure data, perform statistical analysis, and build algorithmic solutions to reduce fraud, monitor our buyers and sellers, and intermediate payments to improve the overall eBay experience. As a member of the decision science team, you will research and develop new methodologies and techniques to improve the overall effectiveness of risk management. Mine and analyze massive amount of unique internal and external data to gain deep business knowledge and insight on customer activity and usage behaviors and their relationships with fraud, credit risks, and other types of behaviors. Acts as the technical owner of projects that may require significant customization of existing analytic tools, techniques, processes or development of new ones. Perform statistical data analysis and understanding, ensure data quality, and develop tracking and reporting systems to determine the effectiveness of models, rules, and other risk initiatives and programs. Design and create systems to structure, aggregate, and turn petabytes of messy information into statistically significant features for modeling purposes. Problem sets are focused around fraud and risk management to include models to prevent fraudsters from listing and monetizing on the platform, thwarting registration attacks, and risk scoring our customers.
Required Skills and Experience:
- Bachelor’s degree in a quantitative field: engineering, math, statistics etc. MS/PHD preferred
- MS + 7, PHD + 4 years
- Expert in SQL, relational databases
- Big Data technology: Hadoop framework: Hive, Spark, etc.
- Expertise in machine learning packages Python, R,SAS
- Strong knowledge of 1 or more scripting and programming languages (Python, Java, Scala, etc.)
- Background in a variety modeling techniques: GBM, logistic regression, clustering, neural networks, NLP
- Strong analytical skills with good problem solving ability
- Good presentation and communication skills required
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eBay Inc. is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, veteran status, and disability, or other legally protected status. If you are unable to submit an application because of incompatible assistive technology or a disability, please contact us at firstname.lastname@example.org. We will make every effort to respond to your request for disability assistance as soon as possible.
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