Senior Staff Data Scientist

Company Overview

Job Overview

Intuit’s Global Business Solutions Group (GBSG) is dedicated to creating tools and services that dramatically increases the ability of small-medium businesses (SMBs) to manage cash flow. At the center of this mission, the QuickBooks Money team is developing innovative tools to help customers get paid faster and with confidence. Our expanding suite of offerings and new go-to-market strategies aim to further enhance SMBs' ability to accept payments seamlessly. The Sales Data Science team on the GBSG Data & Analytics organization’s mission is to deliver and drive business impact by developing insights, models, and strategic thought partnership that helps accelerate GBSG’s growth. We partner with Sales, Product, Finance, Business Operations, Design and Engineering teams to foster data-driven decisions through strategic thinking, data analysis, experimentation, and predictive analytics. As a Senior Staff Data Scientist on the Sales Data Science team partnering with our Money Growth Mission Team, you will dive deep into our data to uncover actionable insights and make recommendations that will influence the company’s strategic decisions, especially with improving Sales efforts. The ideal candidate will have a strong background in quantitative analysis using large data sets, demonstrated growth hacking experience, and in data-driven decision making.

Responsibilities

As a Senior Staff Data Scientist on the Sales Data Science team, you will dive deep into our data to uncover actionable insights and shape strategic decisions. Your contributions will directly impact our GTM strategies to address one of SMBs’ biggest challenges: getting paid on time. This role requires a strong background in quantitative analysis, data-driven decision-making, and expertise in working with large data sets.

• Conceptualize business problems or opportunities, formulate hypotheses and goals, key metrics and make actionable recommendations
• Drive strategic insights through effective storytelling with data, to educate and instill confidence, motivating stakeholders to act on recommendations.
• Develop predictive models, conduct experimentation beyond A/B testing, and generate actionable customer insights that inform Sales innovation
• Build and apply durable customer segmentation patterns to renew targeting, positioning, and customer experience
• Partner closely with Sales, Product, Marketing, Engineering, Design, and Analytics leaders to deliver insights that drive product strategy and growth
• Translate complex data insights into actionable recommendations for technical and non-technical stakeholders, and business leaders

Qualifications

The ideal candidate is a curious, proactive data scientist with experience in building scalable solutions, a strong understanding of customer behavior, and a passion for fintech.

• BS or MS degree in Statistics, Mathematics, Operations Research, Computer Science, Econometrics or related eld; equivalent experience will be considered
• 10+ years of experience in data science or analytics, preferably in fintech, with a strong foundation in predictive modeling, customer segmentation, and experimentation
• Ability to formulate data-backed strategies that will drive step-function growth for the business as well as increase customer benefit
• Ability to generate hypotheses grounded in customer behavior, industry trends, and external market factors. Experience in the fintech or SMB space is highly preferred.
• Experience in designing and interpreting complex experiments beyond traditional A/B testing methods
• Demonstrated experience in building reusable and scalable analytics solutions, with a focus on efficiency and avoiding duplication of work
• Outstanding communication skills with the ability to influence decision makers and build consensus with teams

• Quick learner, adaptable, with the ability to work independently or as part of a team in a fast-paced environment
• Experience using statistics and machine learning techniques to solve complex business problems within go-to-market and marketing areas, e.g., propensity for feature adoption, customer health scoring to identify customers at risk to cancel, next best action models etc.

• Technical Skills:

○ Advanced SQL skills and proficiency in visualization tools such as Qlik, Tableau, Plotly Dash ○ Strong analytical and modeling skills using Python (for its rich suite of statistical and modeling libraries like numpy, pandas, scikit-learn, etc.) ○ Familiarity with Linux/OS X command line, version control software (git), and general software development