Website Intuit
Job 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 2023. If you do not meet this 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 Mint) that solve real-world customer problems and power prosperity around the world?
Intuit is seeking Data Science Interns to join us this Summer (2022). 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.
Qualifications
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
Responsibilities
Areas we are exploring:
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)
Sample intern projects from previous years:
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