Data Scientist 2

OverviewIntuit is looking for a Data Scientist to join the Intuit AI team.This team embeds artificial intelligence and machine learning into our product portfolio and business to create smarter products, improve anti-fraud and security and enhance customer care. Come join our collaborative and creative group of data scientists and machine learning engineers and build models that directly affect hundreds of thousands of our customers. In this role you will be building and deploying machine learning models using both analytical algorithms and deep learning approaches.What you'll bring• BS, MS or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research, etc.) or equivalent work experience.• 1+ years of experience in modern data science tools and proficient in Python and typical data science libraries (e.g., TensorFlow, PyTorch, Keras).• Efficient in SQL• Comfortable in Linux environment• 1+ years of experience in machine learning techniques such as classification, regression, decision trees, neural nets, large language models, recommender systems, natural language processing, clustering, anomaly detection, sequential pattern discovery, text mining, and familiar with the latest trends and applications of generative AI.• Solid communication skills: Demonstrated ability to explain complex technical issues to both technical and non-technical audiences.How you will lead• Explore, develop, and deploy machine learning models. You will own the end-to-end ML lifecycle in partnership with machine learning engineers.• Apply various machine learning techniques (supervised, unsupervised, reinforcement learning, NLP, GenAI) to improve relevance and personalization algorithms.• Work side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products.• Discover, process, and train on huge data sets.• In partnership with product managers and analysts, run regular A/B tests, perform statistical analysis, draw conclusions on the impact of your models, and communicate results to peers and leaders.• Explore new technology shifts in order to determine how they might connect with the customer benefits we wish to deliver.