Remote 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 experience
• Experience using statistics and data science (machine learning & AI) to solve complex business problems
• Proficiency in SQL, Python, AWS, 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 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 BILL
• Work 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
• Model development
• Dashboard Creation
• Project Management
• Strong Communication Skills.

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
• 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 time
• Multiple Zoom interviews (2-3) SQL assessment during 1st interview.

MUST 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 experience
• Experience using statistics and data science (machine learning & AI) to solve complex business problems
• Proficiency in SQL, Python, AWS, Excel including key data science libraries
• Proficiency in data visualization including Tableau
• Experience working with large datasets
• Bonus: Experience with development and implementation of AI tools (e.g. LLMs) for risk use cases.