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Staff Fraud and Risk Analyst

Category Security, Risk & Fraud Location Mountain View, California Job ID 2025-69154
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Company Overview

Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.

Job Overview

Intuit Inc. is committed to powering prosperity around the world for consumers, small businesses, and the self-employed through its ecosystem of innovative financial management solutions. To achieve this, the Fintech Risk team is looking for a strong analyst to help prevent risk and support customer growth and protection.

As a Staff Fraud & Risk Analyst, you will be focusing on Data Analytics Capabilities and Deep Dive Analysis within Intuit’s money movement product space. You will partner with a wide range of teams to understand our business, products, data, and data pipelines. You will deep dive into data and business opportunities, deliver data-driven insights, and work with cross functional teams to drive innovative change.

Responsibilities

  • Use advanced data skills, analytics techniques, and predictive analytics in developing insights and analyses
  • Develop statistical model/anomaly detection and investigate anomalies to monitor and help prevent the potential risk/issue in business
  • Develop data capabilities, self-service capabilities, and analytical capabilities to streamline and automate the analytical processes, drive discussions, facilitate decision making, and deliver actionable insights by partnering with stakeholders
  • Apply operations research and statistical methods to optimize operation strategies with ROI evaluations, buffer/queuing management and capacity planning etc.
  • Provide recommendations utilizing multiple types of data, business knowledge, and strategic assumptions when data doesn’t exist
  • Identify opportunities and influence partners to act
  • Ensure data collection and data processing is optimized to provide crystal-clear visibility into the impact and value of new initiatives and product releases

Qualifications

  • Strong business acumen and exceptional analytical ability with 5+ years of industry experience with data analysis/data science/operation research
  • 5+ years of experience in modern advanced analytical tools and programming languages such Python with pandas, matplotlib, and scikit-learn etc. Databricks experience preferred and experience with AWS kit is plus
  • Fluent in Python, SQL, Hive, and analytical BI tools such as Tableau, QuickSight etc.
  • Hands-on expertise with data mining and statistical modeling techniques such as clustering, classification, regression, anomaly detection etc.
  • Ability to simplify stories with data, and be concise in the insights and how they drive outcomes with leadership and stakeholders
  • Proactive and inquisitive learner, seeks out and capitalizes on opportunities for change that enhance the business, rather than reacting to circumstances
  • Outstanding communications skills with both technical and non-technical colleagues, with a proven ability to think critically, iterate quickly, and partner across a wide variety of functions to drive changes
  • BS, MS or PhD in Statistics, Mathematics, Computer Science, Economics, Operations Research, or equivalent
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Intuit provides a competitive compensation package with a strong pay for performance rewards approach. The expected base pay range for this position is Bay Area California $172,000 - 232,500. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.

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