Senior AI Scientist

Intuit

San Diego (CA)

On-site

USD 160,500 - 217,000

Full time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

Benefits offered by this job

Cash bonus

Equity rewards

Comprehensive benefits package

Job summary

A leading software company in San Diego is looking for a Senior AI Scientist to innovate and improve operational efficiency. You will utilize advanced AI and ML techniques to solve complex challenges such as demand forecasting and resource allocation while collaborating across teams to design effective experiments. The ideal candidate will have a strong background in Python or R, significant experience in data science, and expertise in deep learning and reinforcement learning techniques. This full-time role offers a competitive salary and a dynamic work environment.

Qualifications

4+ years of industry experience with Data Science/AI (or 2+ years with a PhD).
Strong proficiency in Python or R, with expertise in modern analytical libraries.
Comfortable working in a Linux environment.

Responsibilities

Perform hands-on data analysis and modeling with massive datasets.
Apply data mining, NLP, and machine learning to improve relevance.
Design algorithms for operational problems including demand forecasting.

Skills

Python

Machine Learning

Data Analysis

SQL

Deep Learning

Reinforcement Learning

Communication

Education

MS or PhD in a quantitative field

Tools

scikit-learn

pandas

numpy

Hive

SparkSQL

Job description

Join to apply for the Senior AI Scientist role at Intuit

Overview

Intuit is seeking an innovative and hands‑on Senior AI Scientist to join the Ops Science team. This team is at the intersection of Artificial Intelligence, Machine Learning, and Operations Research. We embed advanced algorithms into our business to create smarter products, improve security, and—crucially—optimize the operational efficiency of our massive customer success networks. In this role, you will move beyond standard classification problems. You will build and deploy models that solve complex resource allocation, forecasting, and scheduling challenges. You will predict demand, optimize workforce capacity, and enable real‑time decision‑making that directly impacts hundreds of thousands of customers and experts. You will partner closely with product managers, software engineers, and designers to design experiments and Minimum Viable Products (MVPs). Your role will range from research experimentalist to technology innovator to consultative business partner.

Responsibilities

End-to-End Modeling: Perform hands‑on data analysis and modeling with massive datasets. Discover data sources, build ETL pipelines to clean/import data, and make them “model‑ready.”
Advanced Algorithm Development: Apply data mining, NLP, and machine learning (supervised and unsupervised) to improve relevance and personalization.
Optimization & Forecasting: Design and implement algorithms for complex operational problems, including demand forecasting, capacity planning, and resource scheduling.
Experimentation: Work side‑by‑side with cross‑functional teams to design A/B tests, analyze results, draw statistical conclusions, and communicate actionable insights to peers and leadership.
Feature Engineering: Create and refine features from underlying data, developing the subject matter expertise required to build intuition on model performance.
Innovation: Explore new design shifts and technology trends (such as GenAI or Reinforcement Learning) to determine how they can solve customer problems or improve operational efficiency.

Qualifications

MS or PhD in a quantitative field (Computer Science, Statistics, Applied Math, Operations Research, Physics, etc.).
4+ years of industry experience with Data Science/AI (or 2+ years with a PhD).
Strong proficiency in Python or R, with expertise in modern analytical libraries (e.g., scikit‑learn, pandas, numpy).
Proficiency in SQL and experience working with large‑scale data ecosystems (Hive, SparkSQL, etc.).
Comfortable working in a Linux environment.
Solid foundation in standard data mining and statistical modeling techniques: clustering, classification, regression, decision trees, neural nets, SVMs, and anomaly detection.
Communication: Demonstrated ability to explain complex technical issues and trade‑offs to both technical and non‑technical audiences.
Advanced Time Series Forecasting: Expertise in applied forecasting (ARIMA, Prophet, neural methods) with an ability to handle sparsity, hierarchical constraints, and recurrent events to predict business volumes or attrition.
Reinforcement Learning (RL): Interest or experience in RL to move from static optimization to real‑time, dynamic decision‑making systems.
Deep Learning: Experience applying Deep Learning techniques to memory‑constrained problems or complex pattern recognition.
Domain Expertise: Background in Workforce Management (WFM), Supply Chain, Logistics, or Gig‑Economy dynamic pricing/matching (e.g., experience at companies like Uber, DoorDash, Amazon Supply Chain, or Airline Revenue Management).

Compensation

Intuit provides a competitive compensation package with a strong pay‑for‑performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs. 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.

Base pay range: