Associate Fraud Risk Data Scientist

Note: 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.

Job Description
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.

We'd 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.

Bachelor's/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
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.

Bachelor's/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.