Experience Level: Mid-Senior
Experience Required: 3 Years
Education Level: Bachelor's degree
Job Function: Finance
Industry: Financial Services
Relocation Assistance: No
Visa Sponsorship Eligibility: No
Note: This is a hybrid position; must be based in the San Jose area.
Job Description:
We are seeking a talented, enthusiastic, and dedicated individual to support the Fraud Risk Strategy team. The incumbent will be responsible for supporting key projects related to fraud detection, risk analysis, and loss mitigation. This position requires an individual with experience in performing analytics, refining risk strategies, and developing predictive algorithms, preferably within the risk domain.
We'd love to talk if you have:
- Maximum 2 years of experience in risk analytics, data analysis, and data science within the relevant industry, with expertise 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, and 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 the development of dashboards and visualizations, i.e., 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 significant impact.
- Desirable to have experience or aptitude in 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, and fraud typologies
Key Job Functions:
- Design rules to detect/mitigate fraud
- Develop Python scripts and models that support strategies
- Investigate novel/significant cases
- Identify root cause
- Set a strategy for different risk types
- Work with product/engineering to improve 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 address emerging fraud trends but also provide a seamless experience for 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, presenting findings effectively at multiple levels of stakeholders.
- Development of a dashboard and visualizations to track the 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-year period.
- Potential to be extended based on business needs and performance.
- Day Shift: Monday through Friday Pacific time
- Multiple Zoom interviews (2-3) – SQL assessment during 1st interview.
Must Haves:
- Maximum 2 years of experience in risk analytics, data analysis, and data science within the relevant industry, with expertise 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 with SQL, Python, and 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.