Associate Fraud Risk Data Scientist

Position Title: Fraud Risk Data Scientist
Location: Hybrid – San Jose, CA (Remote considered if no viable local candidates)
Employment Type: Contract (1 year, potential extension based on business needs and performance)
Work Schedule: Monday – Friday, Pacific Time

About the Role

We are seeking a talented and dedicated Fraud Risk Data Scientist to join our Risk Data & AI Innovation team. The ideal candidate will have a strong background in machine learning, data science, and analytics—specifically in risk and fraud detection. You will play a key role in designing, developing, and implementing AI and ML models to detect and mitigate fraud, while driving AI transformation within the risk management space.

Responsibilities

• Design and develop machine learning and AI models to detect and mitigate fraud and risk.

• Perform data analysis, statistical modeling, and model monitoring to ensure accuracy and efficiency.

• Collaborate with cross-functional teams including product, engineering, and operations to deploy and refine models in real time.

• Support stakeholders in effective model utilization and integration into business processes.

• Develop dashboards and visualizations (e.g., Tableau, AWS Quicksight) to track key performance metrics.

• Communicate analytical findings and model insights to technical and non-technical audiences, including executives.

• Drive AI innovation within risk management activities, leveraging advanced data science and analytics.

Requirements

• Experience: 2–6 years in machine learning/AI, data science, or risk analytics, ideally in industries such as eCommerce, online payments, user trust/risk/fraud, or investigation/product abuse.

• Education: Bachelor’s or Master’s degree in Data Science, Data Analytics, Mathematics, Statistics, Data Mining, or related field (or equivalent practical experience).

• Technical Skills:

  • Strong proficiency in SQL and Python (including key data science libraries).

  • Experience with AWS and data visualization tools such as Tableau or AWS Quicksight.

  • Proficient in handling large datasets and performing complex data manipulations.

• Analytical Skills: Ability to apply statistical and machine learning techniques to solve complex business problems.

• Communication: Proven ability to clearly articulate technical results to both technical and business stakeholders.

• Mindset: Comfortable with ambiguity, capable of turning unclear problems into measurable business outcomes.

Preferred Qualifications

• Experience or aptitude in applying AI and data science solutions to risk and fraud problems.

• Familiarity with AI tools such as LLMs (Large Language Models) for risk-related use cases.

• Strong project management and stakeholder collaboration skills.