Intuit Staff Data Scientist 

United States, California, San Diego 

202898829

We are looking for a strategic and organized leader with a passion for innovation and data-driven optimization. As a Staff Data Scientist, you will work alongside senior leaders from the Technology organizations to deliver insights that lead to faster decisions, increased execution velocity and predictive actions.

• Use advanced analytics techniques and predictive analytics in developing insights and analyses
• Develop data capabilities, self-service capabilities, and analytical capabilities to streamline and automate the analytical processes
• Provide recommendations utilizing multiple types of data, business knowledge, and strategic assumptions when data doesn’t exist.
• Develop statistical model/anomaly detection and investigate anomalies to monitor and help prevent the potential risk/issue in business.
• Need strong data storytelling skills, with the ability to construct impactful visualizations, communicate insights, and influence leadership.
• Lead in a proactive and results-oriented manner within an ambiguous fast-paced environment.
• Evaluate tradeoffs in design and apply a decision making framework that leverages the right solution for a given business use case
• Analyze large, complex datasets, create and maintain clean data standards to ensure accuracy of both data and for teams to leverage.
• Prioritize and manage stakeholder expectations, working to develop a joint roadmap, balancing short term critical needs with long term planning and development
• Develop analytical tools and programs to unlock data as an asset
• Effectively and proactively communicate cross-functionally and at a variety of altitudes
• Have proficiency in SQL, data visualization tools (Qlik, Tableau, QuickSight), scripting languages (Python, R) and code deployment, change management and collaboration technologies and frameworks (GIT, Jenkins, CICD).

• Strategic Thinking & Business Acumen
  • Demonstrate ability to turn business strategy into business problems, iteratively self-generate hypothesis using data, validate or disprove hypothesis to create actionable insights and recommendations that inform decision making
  • Understands the business strategy and able to drive business case development independently for multiple initiatives using data and strategic thought leadership
  • Able to develop complex learning plans that account for different outcome-based scenarios and provide thought leadership on alternative paths
  • Able to define metrics at the business segment level (e.g.: TT Live, QB Live) in a Business Unit
  • Combine insights, business acumen and strategic considerations to influence cross-functional leaders (e.g.: Directors+)

• Strategy & Measurement for AI Native Experiences
  • Ideate new AI solutions by connecting industry-wide developments in AI with strategic insights, customer knowledge and domain knowledge.
  • Influence cross-functional team in creating a development plan at the business segment or functional group level in a Business Unit
  • Understands the technical architecture powering AI-native experiences (e.g.: GenOS) and the various technology capabilities (e.g. GenOrchestrator, LLMs, GenStudio).
  • Able to identify implications to data, analysis methods and customer behaviors because of these architectural constructs
  • Develops a learning plan by connecting model performance metrics with customer and business metrics.
  • Able to offer alternative strategies of testing and measuring AI-native experiences that account for safety, risk and ethical considerations

• Inference & Algorithms
  • Ability to manage E2E aspects of a complex analytics model/project build (i.e., scope, requirements gathering/finalizing, stakeholder management, communication, project management) that drives BU decision making.
  • Advanced knowledge of data preparation, analysis and modeling using Generative AI tools, data and programming tools (SQL/Python/R) , and data workflow management (authoring, scheduling and monitoring) tools
  • Advanced knowledge of designing complex web experiments (A/B/n, painted-door, bandits etc.) and measure their impact using descriptive and inferential statistical methods.
  • Drive an iterative experimentation culture by combining quantitative insights, strategic thinking and qualitative learnings that increase learning and success rate.