Senior Data Scientist

Company Overview

Job Overview

Intuit’s Global Business Solutions Group (GBSG), a global team serving the needs of over 2 million small businesses and self-employed. We are rethinking how to power prosperity around the world. This means dramatically rethinking how we can enable small businesses and individuals to run their businesses with confidence and save time by using both our cloud solutions and our app partners.

The Data Analytics & Science team drives user growth and retention using product insights and marketing channel optimization for the Global Business Solutions Group. We are an exciting, growing and fun team that works with industry- leading analytics tools, techniques and best practices.

​​We are looking for a candidate with deep experience in marketing-centric data modelling and optimization. As a senior data scientist in the marketing science team, you will play a critical role in informing marketing strategy and decision making. You will work cross functionally with marketing, sales, finance, other analytics and engineering teams to drive measurement and optimization of growth marketing efforts.

Responsibilities

• Develop predictive models to apply on QuickBooks marketing site and in product to personalize experience for potential and existing customers, driving better acquisition and retention.
• Create solutions to evaluate ROI for new pricing and strategy initiatives.
• Lead analytics for big campaigns/initiatives/tests measurements.
• Must have good judgment with the ability to think creatively and strategically.
• Communicate results and share actionable insights clearly, concisely and confidently to the broad team and leadership.

Qualifications

• MS/PhD in quantitative fields such as Business, Math, Statistics, Finance, Economics, Operations Research or a related field with 3+ years working experience in an analytics related role.
• Proficient in one of the following analytics tools, such as Python and R.
• Efficient in SQL, Hive, SparkSQL, etc.
• Good understanding of customer life cycle and lifetime revenue.
• Deep understanding of statistical modeling and marketing measurement, and a track record of solving problems with these methods.
• Confident with machine learning techniques and sophisticated analytics (e.g. regression, classification, clustering, time series, econometrics, mathematical optimization)
• Excellent communication skills and ability to learn fast, and confidence in taking ownership.