Associate Fraud Strategy Data Scientist

KinNew Solutions • San Jose, CA, US

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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. 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

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.

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Data Scientist • San Jose, CA, US