This role pays more than
53%
of similar roles. Most pay
$166,500–$246,500
— the shaded band above.
At the midpoint, this role pays about
$212k
versus about
$206k
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
210 open roles
on FindRole.
Listed pay typically runs
$202,500–$274,000
across 187 roles with salary data.
JOB TITLE: Senior AI Scientist - Consumer Fraud Risk
As a Senior AI Scientist - Consumer Fraud Risk, you will join the Consumer Risk AI Science team to mitigate credit and fraud risk while minimizing impact on good customers. You will design, build, evaluate, monitor, and maintain machine learning models to predict and prevent various types of credit and fraud risk in consumer money products. Your day-to-day involves owning the full model lifecycle, collaborating with Risk Policy, Operations, Product, Engineering, and Compliance teams, and contributing to scalable ML and data infrastructure. You will utilize Python, SQL, TensorFlow, and PyTorch to apply deep learning, tree-based models, reinforcement learning, clustering, time series, causal analysis, and natural language processing. You will also research innovative statistical approaches to solve dynamic, real-world fraud challenges within the consumer lending and banking product space.
Design, build, evaluate, monitor, and maintain machine learning models to predict and prevent credit and fraud risks.
Own the full model lifecycle from initial research and development through deployment and performance monitoring.
Lead fraud risk modeling for evolving consumer money products and manage program-level outcomes.
Develop scalable ML and data infrastructure to improve the speed and reliability of fraud modeling efforts.
Shape and implement a unified data strategy to streamline access and usability for risk and fraud use cases.
Research and apply innovative machine learning and statistical approaches to solve dynamic, real-world fraud challenges.
Contribute to the technical strategy and decisioning roadmap for risk across multiple product lines.
What we're looking for
Master's degree or higher in Computer Science, Data Science, AI, Mathematics, Statistics, Physics, or a related quantitative discipline.
Ph.D. in Computer Science, Data Science, AI, Mathematics, Statistics, Physics, or a related quantitative discipline (preferred).
3+ years of experience in Data Science, Machine Learning, and related areas.
Deep understanding of machine learning techniques including deep learning, tree-based models, reinforcement learning, clustering, time series, causal analysis, and NLP.
Proficiency in deep learning ML frameworks such as TensorFlow or PyTorch.
Authoritative knowledge of Python and SQL.
Expertise in designing and building efficient and reusable data pipelines and frameworks for machine learning models.
Relevant work experience in money fraud/credit risk, banking, finance, or fraud detection data (preferred).
Python
Spark
Ray
H2O
PyTorch
Kubernetes
AWS
Conda
Scala
R
Machine Learning
Deep Learning
Relational Databases
Big Data
Time Series
Sentiment Analysis
Clustering
Classification