Data Science Intern

Overview

This position is intended for students in a degree-seeking program, to which they will return at the end of the summer internship in 2024. If you do not meet these minimum criteria, please revisit our careers site for other opportunities.

Want to apply your technical skills and innovative ideas on top of the collective financial data of 40+ million consumers and small businesses, and help build data products (in TurboTax, QuickBooks, and Credit Karma) that solve real-world customer problems and power prosperity around the world?

Intuit is seeking Data Science Interns to join us this Summer (2024). Working side-by-side with Intuit's Data Scientists and Machine Learning Engineers - along with Data Analysts, Software Engineers, and product managers - you will help us understand and improve customer experiences with our products by uncovering critical insights and developing machine learning models.

• Times series forecasting
• Knowledge Engineering
• Reinforcement Learning/Bandits/Causal Inference
• Image/document understanding
• Intent classification
• NLP/NLU/NLG
• Conversational UI, Chatbots
• Personalization and recommendation
• Deep learning
• Semi-supervised learning
• ML services (autoML, feature recommendation, explainable AI, etc)

• Must be currently enrolled in a degree seeking program and return to school after internship is complete
• PhD in Computer Science or related technical field (will consider Masters students with previous experience)
• Familiar with machine learning techniques (regression, classification, clustering, optimization, etc) and understand their mathematical foundations
• Ability to explore, discover and import data from multiple sources and make them machine learning ready
• Design and test hypotheses about causes and cures
• Strong programming skills (Python and Scala preferred)
• Excellent communication skills and ability to learn fast
• Experience in developing machine learning solutions to solve real-world problems is a plus
• Experience with Hadoop or Spark is a plus
• Published works in top tier data science and machine learning conferences such as KDD, ICML, NIPS, ICLR, ACL, SIGIR, WWW, CVPR, SIGMOD, etc. is a plus

• Adversarial Deep Learning ranking algorithms for question-answer forums
• Use topic modeling to link form lines to verbose instruction/publication documents
• Patch-based information extraction using an unsupervised form segmentation algorithm
• Assess agent call quality using call transcript data
• Apply deep learning for transaction time series forecasting with uncertainty estimation
• Explore active learning to improve the event labels used to train our supervised models
• Predict cognitive biases using financial data
• A Deep-Learning Approach to building Temporal Recommendation Models
• Real-Time Churn Prediction Models
• Developing an unsupervised knowledge acquisition for question answering with Pre-trained Language Models
• Developing a customer intent classifier model for Intuit Digital Assistant