Senior Staff Data Scientist - Fintech Analytics

Category Data

Location
Mountain View, California
Job ID 21649

Apply Now

Share Job
Save JobSaved Job

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

At Intuit, we are building the financial operating system that powers money movement across our ecosystem—QuickBooks, TurboTax, Credit Karma, and Mailchimp. The Fintech Platform team sits at the center of this mission, enabling secure, reliable, and scalable movement of billions of dollars annually across payments, banking, balance, lending, and global rails.

We are seeking a Sr. Staff Data Scientist, Fintech Analytics to drive data science and decision intelligence for our money movement platforms. This is a highly visible, high-impact individual contributor role responsible for transforming complex transactional data into predictive insights that improve reliability, revenue, risk management, and customer trust.

This role goes far beyond dashboards or retrospective analysis. You will be a deep technical expert and trusted analytical partner to Fintech product, engineering, risk, finance, and operations leaders — delivering rigorous analysis and models across performance, fraud, declines, funds availability, and platform health. Your work will directly inform platform architecture decisions, risk posture, investment prioritization, and long-term growth across the Fintech organization.

Responsibilities

• Execute Sr. Staff-level data science work across Intuit's Fintech money movement platforms, spanning payments, balance, banking, lending, and global rails — delivering high-quality, high-impact analytical solutions with a high degree of autonomy
• Design and build predictive, diagnostic, and AI-powered models to understand and improve transaction success, authorization rates, latency, fraud losses, and funds availability at scale
• Contribute to platform-wide observability from a data science lens — connecting system behavior to customer impact, revenue outcomes, and risk exposure across fintech domains
• Collaborate closely with Product, Engineering, Risk, Finance, and Operations stakeholders to deliver data-informed analysis and recommendations that shape strategy and trade-offs
• Own root-cause analysis for complex, ambiguous, and high-stakes issues such as payment declines, anomaly detection, fraud spikes, reconciliation gaps, and cross-rail performance differences
• Apply and contribute to frameworks that quantify business impact during incidents and systemic changes, enabling faster and better decision-making across Fintech
• Translate model outputs and predictive insights into clear narratives, scenarios, and actionable recommendations that inform roadmap and investment decisions
• Contribute to and uphold best practices for model governance, metric definitions, experimentation rigor, and AI enablement within the Fintech data science team
• Support data enablement efforts — including contributing to self-service analytics, golden datasets, and tooling adoption (Databricks, Amplitude, Tableau) — that scale analytical capability across the organization
• Share expertise and elevate peers through technical collaboration, code and model reviews, and knowledge sharing

Qualifications

• 8+ years of experience in data science, applied machine learning, or quantitative analytics, with demonstrated ownership of complex, large-scale analytical systems
• Strong experience working with large-scale transactional or event-driven data in fintech, payments, banking, or other regulated financial domains
• Solid understanding of money movement concepts including payment rails, authorization flows, settlement, reconciliation, fraud, chargebacks, and funds availability
• Proven track record of building models and analytical systems that drive measurable, real-world business outcomes — not just offline or academic results
• Advanced proficiency in SQL and Python (or equivalent) for data science, modeling, and production-grade analytics engineering
• Experience with anomaly detection, forecasting, classification, causal analysis, and optimization in production environments
• Ability to work within and contribute to experimentation frameworks, model governance standards, and metric definitions across teams
• Strong ability to translate complex analyses and model outputs into clear, actionable insights for technical and non-technical stakeholders
• Comfortable operating in ambiguity, taking ownership of problems end-to-end from formulation through impact
• Collaborative partner who can influence through expertise — building credibility and alignment with engineering, product, risk, and finance stakeholders
• Bachelor's degree in a quantitative field (Engineering, Computer Science, Mathematics, Statistics, or equivalent); advanced degree preferred

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may 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. The expected base pay range for this position is:

Bay Area California $ 210,500- 284,500

Apply Now
Save JobSaved
Share Job

For more details click Job Post.