Intuit Inc.
Senior Manager, Data Analytics
Intuit Inc., San Diego, California, United States, 92189
We are looking for an experienced and highly-motivated Senior Manager to lead our Business Model & Monetization Data Science team. You will lead a team of data scientists and analysts responsible for developing and utilizing data to drive insights and decisions informing TurboTax’s product lineup, SKU inclusions, and pricing strategy to drive improved market share and total product revenue.
The ideal candidate must have a strong background in data science, inference, automation and experimentation, be able to communicate effectively with stakeholders, and have a passion for driving data-informed solutions.
Responsibilities
Lead a team of data scientists and analysts in the development and execution of monetization analytics projects to support revenue growth and profitability. Build A/B testing and experimentation frameworks to optimize pricing, product design, packaging, and ad placements across all customer segments. Develop machine learning models to build predictive models for ad monetization and subscription retention for different customer cohorts. Drive initiatives to identify user trends and behavior patterns, and develop conversion and retention strategies accordingly. Collaborate with cross-functional stakeholders, such as engineering, product management and marketing teams, to identify opportunities for monetization growth. Manage monetization data pipelines by identifying automation opportunities for data collection, extraction, and management to improve data quality, consistency, and scalability. Analyze and report on monetization metrics such as ARPU, CPI, LTV, churn, and ROI, to provide actionable insights to the executive team and generate business models that can be used to evaluate alternatives. Advise the business on how to use data to support their strategic goals and challenging the stakeholders with insights that drive business decisions. Hire, train and mentor a team of data scientists, analysts, and ensure they stay up to date with industry standards, technology, and best practices. Think in a high-level way about monetization strategies, i.e: subscription-based models, advertising, bundling, and recommend which model suits the client's business. Minimum Requirements
5-7 years of experience leading analytics and data science teams. Proven ability to apply scientific methods to solve real-world problems on web-scale data. Strong business and product sense: delight in shaping vague questions into well-defined analyses and success metrics that drive business decisions. Excellent communication skills: able to lead initiatives across multiple product areas and communicate findings with leadership and product teams. Experience leading key technical projects and substantially influencing the scope and output of others. Knowledge to translate quantitative findings into actionable insights and influence business decisions. Meaningful time as a data scientist partnering with business functions, ideally with paid marketing teams, and having expertise in analyzing brand campaigns. Ability to draw on experience to identify where data can have the most impact and clearly communicate findings and recommendations to partner teams. Ability to turn ambiguous questions and problems into clear deliverables and insights. Experience in querying and manipulating large data sets. Ability to make tradeoffs between speed and accuracy wisely. Strong background in statistical analysis, programming, and experimentation. Expertise in SQL and Hive and at least one scripting language (ideally Python/R), with experience building models in either language. Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, or related field. MS in a quantitative field such as computer science, statistics, economics, math, or equivalent work experience + BS.
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Lead a team of data scientists and analysts in the development and execution of monetization analytics projects to support revenue growth and profitability. Build A/B testing and experimentation frameworks to optimize pricing, product design, packaging, and ad placements across all customer segments. Develop machine learning models to build predictive models for ad monetization and subscription retention for different customer cohorts. Drive initiatives to identify user trends and behavior patterns, and develop conversion and retention strategies accordingly. Collaborate with cross-functional stakeholders, such as engineering, product management and marketing teams, to identify opportunities for monetization growth. Manage monetization data pipelines by identifying automation opportunities for data collection, extraction, and management to improve data quality, consistency, and scalability. Analyze and report on monetization metrics such as ARPU, CPI, LTV, churn, and ROI, to provide actionable insights to the executive team and generate business models that can be used to evaluate alternatives. Advise the business on how to use data to support their strategic goals and challenging the stakeholders with insights that drive business decisions. Hire, train and mentor a team of data scientists, analysts, and ensure they stay up to date with industry standards, technology, and best practices. Think in a high-level way about monetization strategies, i.e: subscription-based models, advertising, bundling, and recommend which model suits the client's business. Minimum Requirements
5-7 years of experience leading analytics and data science teams. Proven ability to apply scientific methods to solve real-world problems on web-scale data. Strong business and product sense: delight in shaping vague questions into well-defined analyses and success metrics that drive business decisions. Excellent communication skills: able to lead initiatives across multiple product areas and communicate findings with leadership and product teams. Experience leading key technical projects and substantially influencing the scope and output of others. Knowledge to translate quantitative findings into actionable insights and influence business decisions. Meaningful time as a data scientist partnering with business functions, ideally with paid marketing teams, and having expertise in analyzing brand campaigns. Ability to draw on experience to identify where data can have the most impact and clearly communicate findings and recommendations to partner teams. Ability to turn ambiguous questions and problems into clear deliverables and insights. Experience in querying and manipulating large data sets. Ability to make tradeoffs between speed and accuracy wisely. Strong background in statistical analysis, programming, and experimentation. Expertise in SQL and Hive and at least one scripting language (ideally Python/R), with experience building models in either language. Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, or related field. MS in a quantitative field such as computer science, statistics, economics, math, or equivalent work experience + BS.
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