below marketmost similar roles pay hereabove market
This role pays more than
72%
of similar roles. Most pay
$162,000–$220,023
— the blue band above.
At the midpoint, this role pays about
$212k
versus about
$191k
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
255 open roles
on FindRole.
Listed pay typically runs
$202,500–$274,000
across 236 roles with salary data.
The Senior AI Scientist will join the Virtual Expert Platform team to serve as the technical architect for recommending, ranking, and personalizing expert connections across TurboTax, QuickBooks, and Credit Karma. You will own recommendation models end-to-end, from data discovery and ETL pipeline construction to production deployment and A/B experimentation. Your daily work involves building multi-stage architectures including candidate retrieval, scoring, and re-ranking to balance immediate revenue with long-term customer trust. You must utilize Python, SQL, PyTorch, TensorFlow, scikit-learn, pandas, and NumPy within Hive and Spark ecosystems. The role requires applying statistical modeling, neural networks, and deep learning techniques like transformers or two-tower models to solve complex matching problems and explore emerging generative AI and reinforcement learning patterns for real-time decision-making.
Design, build, and deploy large-scale recommendation and ranking models to predict customer needs across multiple product verticals.
Architect multi-stage recommendation systems including candidate retrieval, scoring, re-ranking, and real-time blending.
Develop recommendation logic that balances immediate revenue with long-term customer trust and lifetime value.
Perform hands-on data analysis, including building ETL pipelines and cleaning massive datasets for model readiness.
Engineer and iterate on features using domain understanding and statistical rigor to improve model performance.
Design and run A/B tests to perform rigorous statistical analysis and communicate actionable insights to leadership.
Explore and evaluate emerging techniques like generative AI and reinforcement learning to improve recommendation quality.
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 a production environment (consumer tech, fintech, or e-commerce preferred).
Expert proficiency in Python and SQL with deep experience in PyTorch, TensorFlow, scikit-learn, pandas, and NumPy.
Strong experience with ranking, retrieval, and multi-stage recommendation architectures.
Solid foundation in statistical modeling and ML techniques including classification, regression, clustering, neural networks, and recommender systems.
Proficiency working with large-scale data ecosystems such as Hive, Spark, or SparkSQL.
Demonstrated ability to explain complex technical concepts to non-technical audiences and link model performance to business outcomes.
Deep learning expertise in recommendation, personalization, or representation learning (preferred).
Python
PyTorch
TensorFlow
Deep Learning
Reinforcement Learning
LLMs
Probabilistic Modeling
Causal Inference
Multi-Agent Systems
Neuro-symbolic AI
Time-series Forecasting
Simulation-based Planning
Model Training
Data Science
Machine Learning
Machine Learning
Deep Learning
Recommender Systems
Ranking Systems
Multi-objective Optimization
Feature Engineering
Time-series Forecasting
Sequence-to-Sequence Modeling
Survival Analysis
Production Infrastructure