Senior AI Scientist

Intuit

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: San Diego, CA
Salary: $165,500–$223,500 / yr
Posted: 6 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $195k

This role $194k

$130k most similar roles pay here $237k

This role pays more than 59% of similar roles. Most pay $162,000–$227,100 — the shaded band above. At the midpoint, this role pays about $194k versus about $195k 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 200 open roles on FindRole.

Listed pay typically runs $202,500–$274,000 across 176 roles with salary data.

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At a glance

TL;DR · Senior AI Scientist

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As a Senior AI Scientist within the Trust & Safety organization, you will own fraud detection models end-to-end to combat adversarial threats like account takeover and product abuse. You will be responsible for the entire lifecycle of model development, including data discovery, feature engineering, production deployment, and monitoring in live environments. Your work involves building real-time risk scoring, entity-level and graph-based anomaly detection, and applying generative and agentic AI to automate investigation workflows. To succeed, you must possess expert proficiency in Python, SQL, and frameworks such as PyTorch, TensorFlow, scikit-learn, and XGBoost. You will also utilize Spark, Databricks, and Hive within a Linux environment. The role focuses on solving complex fraud problems by balancing detection accuracy with minimal customer friction across the entire user lifecycle.

Skills

Python SQL Machine Learning PyTorch TensorFlow scikit-learn XGBoost Spark Databricks Hive LLM Graph Neural Networks MLOps ETL Anomaly Detection Feature Engineering Linux Statistics

What you'll do

Design and deploy machine learning models to detect fraud and abuse across the customer lifecycle in real-time and batch settings.
Manage the full model lifecycle including data discovery, feature engineering, training, production deployment, and monitoring.
Develop advanced detection methods such as graph analysis, unsupervised clustering, and behavioral modeling for novel attack patterns.
Monitor deployed models for drift and build automated feedback loops for rapid retraining and improvement.
Apply generative and agentic AI to classify unstructured signals and automate manual investigation workflows.
Design experiments and champion/challenger tests to determine optimal operating thresholds and downstream actions.
Translate model performance into business implications for policy, product, engineering, and compliance stakeholders.

What we're looking for

MS or PhD in Computer Science, Statistics, Applied Mathematics, Operations Research, Physics, or a related quantitative discipline.
4+ years of industry experience building and deploying machine learning models in production.
Expert proficiency in Python and SQL with deep experience in modern ML/DL frameworks like PyTorch, TensorFlow, and scikit-learn.
Experience handling severe class imbalance, delayed labels, and evaluation metrics beyond accuracy such as precision-recall trade-offs.
Proficiency with large-scale data ecosystems including Spark, Databricks, or Hive within a Linux environment.
Demonstrated ability to explain complex technical concepts and model performance to both technical and non-technical stakeholders.
Experience in fraud, risk, abuse, security, or adversarial modeling domains (preferred).
Experience with graph-based methods, real-time model serving, MLOps, or applied LLM experience in production (preferred).

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