Senior AI Scientist, Fintech Risk AI

Intuit

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Quick summary

Work type
On-site
Location
Mountain View, CA
Salary
$180,000–$243,500 / yr
Posted
23 days ago
Freshness
Confirmed live today
Nearby
99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $199k
This role $212k
$141k $255k
below market most similar roles pay here above market

This role pays more than 61% of similar roles. Most pay $162,000–$235,750 — the blue band above. At the midpoint, this role pays about $212k versus about $199k for comparable roles.

Based on 240 similar postings.

Employer

About Intuit

Intuit is a financial software company known for products like TurboTax, QuickBooks, Mint, and Credit Karma, helping consumers and small businesses manage their finances and taxes. Industry: Financial Software & Technology

Intuit currently has 212 open roles on FindRole.

Listed pay typically runs $202,500–$274,000 across 191 roles with salary data.

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At a glance

TL;DR · Senior AI Scientist, Fintech Risk AI

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The Senior AI Scientist joins the Fintech Risk AI/ML Smart Money Services team to detect and prevent fraud and financial risk for all Money Movement events. This role involves leading the full cycle of iterative big data exploration, including hypothesis formulation, algorithm development, data cleansing, and insight visualization. You will collaborate with data engineers and business analysts to design research strategies for extracting insights from complex sources and petabytes of data. Key responsibilities include applying predictive statistical modeling to relational and non-relational NoSQL databases. Required technical skills include Python, R, or Scala, Spark, and experience with GLM, logistic regression, decision trees, and time-series forecasting. You must also possess expertise in Generative AI techniques like GANs and VAEs, as well as supervised and unsupervised machine learning.

Skills

Python R Scala Machine Learning Generative AI Spark NoSQL ETL Data Warehousing Statistical Modeling GLM Logistic Regression Decision Trees Time-series Forecasting Autoencoders GANs Graph Data

What you'll do

  • Lead the full cycle of big data exploration from hypothesis formulation to action planning.
  • Develop and test advanced predictive statistical models using diverse data types and scales.
  • Extract and cleanse data from complex relational and non-relational NoSQL databases.
  • Design and implement research strategies to structure and validate data-driven insights.
  • Apply machine learning techniques to detect and prevent fraud and financial risk.
  • Provide technical guidance to business leaders on harnessing data for critical goals.
  • Interpret and communicate complex research findings to non-technical business stakeholders.

What we're looking for

  • Advanced degree in Statistics, Applied Mathematics, Computer Science, or a related field.
  • 6+ years of experience in developing predictive models and statistical analysis in a financial services or risk management context.
  • Strong proficiency in programming languages such as Python, R, or Scala, and experience with data manipulation and visualization tools.
  • Expertise in statistical modeling techniques such as GLM, logistic regression, decision trees, time-series forecasting, and machine learning algorithms.
  • Expertise and experience with Supervised and Unsupervised ML techniques.
  • Experience in Generative AI techniques, including autoencoders, GANs, and VAEs.
  • Practical experience with ETL processes, data warehousing, and cloud-based computing platforms.
  • Expertise with Spark for data manipulation and ML feature creation.

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