Associate Fraud Risk Data Scientist
5 star recruitmentSan Jose (CA)
About the role
About the job Associate Fraud Risk Data Scientist
JOB DESCRIPTION:
We are looking for a talented, enthusiastic and dedicated person to support the Fraud Risk Data Science Team within the Risk Data & AI Innovation Org. 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 machine learning, model development with cutting edge AI/ML frameworks, performing analytics, statistical analysis and model monitoring. Experience with LLMs and other AI tools would be a big plus. Wed love to chat if you have:2-6 years of experience in machine learning/AI, data science, risk analytics & data analysis within relevant industry experience in eCommerce, online payments, user trust/risk/fraud, or investigation/product abuse. Bachelors/Master's degree in Data Science, Data Analytics, Mathematics, Statistics, Data Mining or related field or equivalent practical experienceExperience using statistics and data science (machine learning & AI) to solve complex business problemsProficiency in SQL, Python, AWS, Excel including key data science librariesProficiency in data visualization including TableauExperience 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 AI and machine learning 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 development and implementation of AI tools (e.g. LLMs) for risk use cases. Key Job Functions: Design and develop machine learning and AI models detect/mitigate fraud Support stakeholders and cross-functional teams in effective usage of models Drive AI transformation for all risk management activities at BILLWork with product/engineering to implement, monitor and refine AI solutions and models Expected Outcomes: Work closely with team members and stakeholders to consult, design, develop, and manage fraud models and AI solutions. Utilize data analysis to design and implement fraud models Collaborate with cross-functional stakeholders including product managers and engineering teams to deploy data-driven fraud models and AI 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 models implemented
Preferred Skills:
- Machine Learning & Artificial Intelligence
- Data Science
- Project Management
- Strong Communication Skills.
- Notes from Hiring Manager:
- Experience applying statistics and data science to tackle intricate business challenges especially in Fraud mitigation
- Proficiency in AWS Quicksight and Tableau
- This is a hybrid position, so candidates must be based in the San Jose area. HM will entertain remote candidates if no viable local candidates can be sourced.
- 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 timeMUST HAVE:2-6 years of experience in machine learning/AI, data science, risk analytics & data analysis within relevant industry experience in eCommerce, online payments, user trust/risk/fraud, or investigation/product abuse.
Bachelors/Master's degree in Data Science, Data Analytics, Mathematics, Statistics, Data Mining or related field or equivalent practical experienceExperience using statistics and data science (machine learning & AI) to solve complex business problemsProficiency in SQL, Python, AWS, Excel including key data science librariesProficiency in data visualization including TableauExperience working with large datasets
Bonus:
Experience with development and implementation of AI tools (e.g. LLMs) for risk use cases.
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