Overview

Come join the AI Smart Products group as a Senior Data Scientist. ‌Our group builds core capabilities to serve the whole ecosystem of Intuit’s products. We use state-of-the-art technologies, as well as building AI-native experiences for our customers.

What you'll bring

• Deep connection to the product and the customers
• Deep interest in cutting-edge innovative technologies in NLP, AI, ML, or DL
• Deep technical understanding of underlying Data Science concepts (not just training models)
• Collaboration with partners across the globe, to deliver complex projects
• Maturity
• Quick learner, adaptable, with the ability to work independently or as part of a team in a fast-paced environment
• Strong verbal and written communication skills. Ability to conduct meetings and make professional presentations, and to explain complex concepts and technical material to non-technical users
  

Advantages

• Experience with NLP, Generative AI
• We welcome people who can deliver E2E AI projects (inception to production). We primarily use Python in all stages of development
• Fluent in SQL enough to get the data you need from a warehouse (Vertica, Hive, SparkSQL)
• Comfortable working in a Linux environment
• Experience with building end-to-end reusable pipelines from data acquisition to model output delivery
  

How you will lead

• Applies proven methods and hacking skills in working with divergent data types, data scales, and big data - to explore and extrapolate data-driven insights using advanced, predictive statistical modeling and testing applied to data acquired and cleansed from a range of sources
• Uses considerable expertise and independent judgment in collaborating with peers, data engineers, database managers, business analysts, architects and product managers in designing and implementing the research strategy needed to methodically and iteratively structure, extract, cleanse, sample, test, validate, and communicate data-driven insights from complex sources and significant volumes of data for complex and unique business problems.
• Provides guidance and support leadership to business leaders and stakeholders, on how best to harness available data in support of critical business needs and goals
• Leads the full cycle of iterative big data exploration, including hypothesis formulation, algorithm development, data cleansing, testing, insight generation and visualization, and action planning
• Provides business stakeholders with entrepreneurial guidance essential for appropriately interpreting and building on findings, and fully exploiting the insights revealed through the research
  

Deliverables

• Business-oriented researchable questions or "working hypotheses" generated in collaboration with business leaders and stakeholders
• Research plans and specifications for large data sets (for example, statistics, sampling strategy, test specification, steps)
• Algorithms that result from the validation of hypotheses, and contribute to useful predictions and insights
• Input product development or marketing strategies
• Analytical inquiry findings