ESR Healthcare
Associate Fraud Strategy Data Scientist San Jose, CA
ESR Healthcare, San Jose, California, United States, 95199
Associate Fraud Strategy Data Scientist
Location: San Jose, CA | Type: Hybrid | Experience: Mid-senior | Education: Bachelor’s degree | Relocation: No | Visa: No
Job Summary:
We are looking for a talented, enthusiastic and dedicated person to support the Fraud Risk Strategy team at Bill.com. The incumbent will be responsible for supporting key projects associated with fraud detection, risk analysis and loss mitigation.
Key Responsibilities
Design fraud rules to detect and mitigate fraud.
Develop Python scripts and models that support fraud strategies.
Investigate novel and large fraud cases.
Identify root causes and set strategies for different risk types.
Work with product and engineering to improve control capabilities.
Develop and present fraud strategies and guide execution.
Qualifications
Maximum 2 years of experience in risk analytics, data analysis, and data science within eCommerce, online payments, user trust/risk/fraud, or investigation/product abuse.
Bachelor’s degree in Data Analytics, Data Science, Mathematics, Statistics, Data Mining or related field.
Proficiency in SQL, Python, Excel including key data science libraries.
Experience using statistics and data science to solve complex business problems.
Experience in data visualization including Tableau.
Experience working with large datasets.
Strong communication and ability to present findings to leadership and cross-functional teams.
Desirable: experience with AWS, payment rule systems, ML teams, fraud typologies.
Preferred Skills
Data analytics and modeling.
Rule development and dashboard creation.
Project management.
Strong communication.
Notes from Hiring Manager:
Strictly contract to cover multiple leaves over a 1 yr period. Potential to extend based on business need and performance. Day shift: M-F Pacific time.
Assessment:
Multiple Zoom interviews (2-3) – SQL assessment during 1st interview.
We expect you to consult, design, develop and manage fraud strategies and rules that provide excellent customer experience and operate at scale and real time.
#J-18808-Ljbffr
Job Summary:
We are looking for a talented, enthusiastic and dedicated person to support the Fraud Risk Strategy team at Bill.com. The incumbent will be responsible for supporting key projects associated with fraud detection, risk analysis and loss mitigation.
Key Responsibilities
Design fraud rules to detect and mitigate fraud.
Develop Python scripts and models that support fraud strategies.
Investigate novel and large fraud cases.
Identify root causes and set strategies for different risk types.
Work with product and engineering to improve control capabilities.
Develop and present fraud strategies and guide execution.
Qualifications
Maximum 2 years of experience in risk analytics, data analysis, and data science within eCommerce, online payments, user trust/risk/fraud, or investigation/product abuse.
Bachelor’s degree in Data Analytics, Data Science, Mathematics, Statistics, Data Mining or related field.
Proficiency in SQL, Python, Excel including key data science libraries.
Experience using statistics and data science to solve complex business problems.
Experience in data visualization including Tableau.
Experience working with large datasets.
Strong communication and ability to present findings to leadership and cross-functional teams.
Desirable: experience with AWS, payment rule systems, ML teams, fraud typologies.
Preferred Skills
Data analytics and modeling.
Rule development and dashboard creation.
Project management.
Strong communication.
Notes from Hiring Manager:
Strictly contract to cover multiple leaves over a 1 yr period. Potential to extend based on business need and performance. Day shift: M-F Pacific time.
Assessment:
Multiple Zoom interviews (2-3) – SQL assessment during 1st interview.
We expect you to consult, design, develop and manage fraud strategies and rules that provide excellent customer experience and operate at scale and real time.
#J-18808-Ljbffr