Associate Fraud Strategy Data Scientist

Position: Associate Fraud Strategy Data Scientist

Location: San Jose, CA, US - (Hybrid 3 days in-office per week)

Contract to Hire: 12 Month Contract

About the Company:

Our client is a leader in financial automation software for small and midsize businesses. They are dedicated to automating the future of finance so businesses can thrive. Hundreds of thousands of businesses trust their solutions to manage financial workflows, including payables, receivables, and spend and expense management.

Position Details:

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

Primary Duties & Responsibilities:

Maximum 2 years of experience in risk analytics, data analysis, and data science within relevant industry experience in e-commerce, online payments, user trust/risk/fraud, or investigation/product abuse.

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

Experience with AWS, knowledge of fraud investigations, payment rule systems, working with ML teams, fraud typologies is a plus.

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

What s Expected:

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

Education / Experience:

Bachelor s degree in Data Analytics, Data Science, Mathematics, Statistics, Data Mining or related field or equivalent practical experience.

MUST HAVE:

Maximum 2 years of experience in risk analytics, data analysis, and data science within relevant industry experience in e-commerce, 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.