Associate Fraud Strategy Data Scientist San Jose, CA

Associate Fraud Strategy Data Scientist San Jose, CA

Fraud Strategy Data Scientist, Risk Data Scientist w/Fraud, Risk Analytics, Data Analysis, Data Science, Fraud Mitigation, Industry: eCommerce, online payments, user trust/risk/fraud, or investigation/product abuse, SQL, Tableau, Data Science Libraries

Experience level: Mid-senior Experience required: 3 Years Education level: Bachelor’s degree Job function: Finance Industry: Financial Services Pay rate : View hourly payrate Total position: 1 Relocation assistance: No Visa sponsorship eligibility: No
Note: This is a hybrid position, so candidates must be based in the San Jose area.

JOB DESCRIPTION:

We are looking for a talented, enthusiastic and dedicated person to support the Fraud Risk Strategy team. The incumbent will be responsible for supporting key projects associated with fraud detection, risk analysis and loss mitigation at Bill.com. This position requires a person who has experience with performing analytics, refining risk strategies, and developing predictive algorithms preferably in the risk domain.

We’d love to chat if you have:

Maximum 2 years of experience in risk analytics, data analysis, and data science within relevant industry experience in 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 or equivalent practical experience
Experience using statistics and data science to solve complex business problems
Proficiency in SQL, Python, Excel including key data science libraries
Proficiency in data visualization including Tableau
Experience working with large datasets
Ability to clearly communicate complex results to technical experts, business partners, and executives including development of dashboards and visualizations, ie Tableau.
Comfortable with ambiguity and yet able to steer analytics projects toward clear business goals, testable hypotheses, and action-oriented outcomes
Demonstrated analytical thinking through data-driven decisions, as well as the technical know-how, and ability to work with your team to make a big impact.
Desirable to have experience or aptitude solving problems related to risk using data science and analytics
Bonus: Experience with AWS, knowledge of fraud investigations, payment rule systems, working with ML teams, fraud typologies
Key Job Functions

Design rules to detect/mitigate fraud
Develop python scripts and models that support strategies
Investigate novel/large cases
Identify root cause
Set strategy for different risk types
Work with product/engineering to improvement control capabilities
Develop and present strategies and guide execution
Expected Outcome in 6-12 months

Work closely with team members and stakeholders to consult, design, develop, and manage fraud strategies and rules that not only solve emerging fraud trends but also provide a great experience to end customers.
Utilize data analysis to design and implement fraud strategies
Collaborate with cross-functional stakeholders including product managers and engineering teams to deploy data-driven fraud solutions that operate at scale and in real time for end customers.
Make business recommendations to leadership and cross-functional teams with effective presentations of findings at multiple levels of stakeholders.
Development of dashboard and visualizations to track KPI of fraud strategies implemented
Preferred Skills

Data analytics and models
Rule development
Dashboard Creation
Project Management
Strong Communication
Notes from Hiring Manager:

Strong SQL proficiency
Experience applying statistics and data science to tackle intricate business challenges especially in Fraud mitigation
Proficiency in AWS Quicksight and Tableau
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
Multiple Zoom interviews (2-3) – SQL assessment during 1st interview.
MUST HAVE:

Maximum 2 years of experience in risk analytics, data analysis, and data science within relevant industry experience in 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 or equivalent practical experience.
Experience using statistics and data science to solve complex business problems.
Experience in SQL, Python, Excel including key data science libraries.
Experience applying statistics and data science to tackle intricate business challenges especially in Fraud mitigation.
Experience in data visualization including Tableau.
Experience working with large datasets.