M2, Fraud and Risk (AI)

TL;DR

M2, Fraud and Risk (AI): Leading and managing a team of Risk and Data Analysts to establish robust monitoring and reporting for consumer banking portfolios with an accent on leveraging big data and AI technologies to optimize analytics solutions. Focus on driving decision support through data-driven insights, collaborating cross-functionally, and mentoring team members to tackle complex fraud and risk detection challenges.

Location: Mountain View, California

Salary: $158,000–$214,000

Company

hirify.global is a global financial technology platform powering prosperity with products like TurboTax, Credit Karma, QuickBooks, and Mailchimp for approximately 100 million customers worldwide.

What you will do

• Lead and manage a team of Risk and Data Analysts focused on monitoring and reporting for consumer banking.
• Collaborate with credit risk, fraud risk policy, product development, and data engineering teams.
• Enable decision support and customer insights in money movement risk and analytics.
• Utilize big data and AI technologies to optimize analytics solutions and policy monitoring.
• Lead and coach the team to drive recommendations using diverse data and business knowledge.
• Mentor other analytics team members across the organization.

Requirements

• Proven leader with 6+ years of experience in data and risk, including 3+ years managing an analytics team.
• Expertise in Python, R, SAS, or SQL.
• Deep understanding of Machine Learning (ML) and Generative AI (GenAI) techniques for credit risk and fraud detection.
• Outstanding communication skills with technical and non-technical colleagues.
• Strong strategic mindset combined with willingness to be hands-on.
• Experience working with BI tools such as Tableau, Quicksight, or Databricks.
• Domain experience in FinTech, Risk, Financial services, with specific expertise in Consumer Banking.

Culture & Benefits

• Competitive compensation package including cash bonus and equity rewards.
• Access to comprehensive benefits plans and programs.
• Focus on pay-for-performance rewards approach.
• Commitment to fair pay through regular comparisons across ethnicity and gender categories.