KYC Data Science Analyst, AML Team

Key Skills

Data Analytics, Data science techniques, Ecommerce, Large data sets, Machine learning techniques, Python Programming, Tableau

Job Description

What You Will Accomplish:

• Recommend KYC enhancements by reviewing large amounts of KYC data to define opportunities. Perform analyses on KYC-related data to identify and mitigate risks to the seller onboarding and verification process.
• Understand AML trends by applying strong analytical skills in order to identify and mitigate potential risks. Partner with the global AML teams to gather information on current AML trends and leverage information to enhance the ARR methodology
• Work with engineering and product teams to understand the end-to-end architecture of ARR including the risk calculation engine and API & ETL processes into the case management gateway.
• Drive the development of business requirements for medium to high complexity processes on ARR and KYC.
• Support continual deep dive and trend analyses of Risk Rating methodology; understand how seller data is mapped to the ARR tool; Ability to test and review logical implementation of ARR.
• Develop internal controls and metrics to continuously monitor trends and data quality; implement processes to detect, correct and prevent invalid data or errors.
• Analyze large volumes of data and conduct tuning to understand the impact of ARR changes to global seller risk scoring and weightage.

What You Will Bring:

• Bachelor’s degree required, preferably BS/BA degree Statistics, Applied Econometrics, Math, Analytics, Computer Science, other related Core Sciences or equivalent.
• 8+ years relevant professional experience or 6 yrs + with a master’s degree
• Strong Analytical and problem-solving skills.
• Solid understanding of AML and KYC. Proficient in SQL and Advanced Excel.
• Expertise in building decision boards using visualization tools like Tableau. Proficienct in Python and a good understanding of data warehouse architecture is required. Should have knowledge on Spark or Hadoop.
• Experience working with system and data architecture, data classification, documenting end-to-end data flow and data lineage.
• Hands-on experience validating the accuracy of SQL logic querying large data sets and ensuring accuracy of data mapping to eBay’s data systems.

Preferred Qualifications:

• CAMS or similar certification.
• Fintech compliance experience.
• Data Science and Machine Learning experience.
• E-commerce domain expertise

The pay range for this position at commencement of employment in California, Washington, or New York is expected in the range below.

$135,200 - $205,700

Base pay offered may vary depending on multiple individualized factors, including location, skills, and experience. The total compensation package for this position may also include other elements, including a target bonus and restricted stock units (as applicable) in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as PTO and parental leave). Details of participation in these benefit plans will be provided if an employee receives an offer of employment.

If hired, employees will be in an “at-will position” and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.

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eBay 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 have a need that requires accommodation, please contact us at talent@ebay.com. We will make every effort to respond to your request for accommodation as soon as possible. View our accessibility statement to learn more about eBay's commitment to ensuring digital accessibility for people with disabilities.