Job Description

The CG TurboTax Data Science org is looking for a strategic and hands-on Data Science Manager to lead our efforts in understanding, segmenting, and growing our customer base. In this role, you will spearhead initiatives that uncover insights into customer behavior, drive personalized marketing strategies, and help shape product and growth decisions. You’ll lead a team of talented data scientists, collaborating cross-functionally with Marketing, Product, Engineering, and Customer Success teams. Responsibilities ● Lead Customer Segmentation Efforts : Design and implement advanced customer segmentation models to identify high-potential customer segments, behavioral clusters, and lifecycle stages.

• Drive Growth Insights : Analyze key drivers of customer acquisition, retention, engagement, and churn. Translate findings into actionable strategies to increase lifetime value and reduce attrition.
• Team Leadership : Mentor, guide, and grow a team of data scientists and analysts. Establish data science best practices and vision for how we should use segmentation to grow our business.
• Measurement Strategy : Design A/B tests, causal inference models, or alternative methods to measure the impact of marketing campaigns and product changes.
• Model Development : Oversee the development of predictive models for customer propensity, churn, and other business-critical KPIs.
• Stakeholder Collaboration : Partner with cross-functional teams to translate business needs into data-driven strategies. Present insights to senior leadership to inform decision-making.
• Tooling & Infrastructure : Collaborate with engineering to ensure the right data infrastructure is in place to support robust, scalable analysis.
• 6+ years of experience in data science or analytics, with at least 2 years in a managerial or team lead capacity.
• Strong background in statistics, machine learning, and predictive modeling.
• Proficiency in SQL and programming languages such as Python or R.
• Experience with marketing analytics, customer segmentation, lifecycle modeling, or growth analytics.
• Experience designing and evaluating A/B tests and causal inference models.
• Excellent communication skills with the ability to translate complex data into business value.

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