Manager 3, Data Science

Job Overview

 Intuit’s Consumer Group, including TurboTax, empowers millions of individuals to take control of their finances. TurboTax simplifies tax preparation and enables our customers to file with confidence. We leverage data and insights to innovate and enhance our consumer offerings.

Now, we are expanding our focus to Consumer Lending within the Consumer Group, including TurboTax, and require a strategic leader to manage and develop a team of decision science for consumer lending. This role will be crucial in supporting our consumer lending initiatives, while navigating the complexities of Fintech Risk, alongside our core tax preparation business. If you are passionate about building and leading a high-performing team that drives results through data-driven insights in consumer lending spaces, and understands the unique risks within Fintech, this role is for you. As a Manager of Data Science in the Fintech Risk team, you will be in a highly influential position to inform Consumer lending product strategy and drive maximum business results.

Responsibilities

• Develop market leading statistical models for the risk department producing risk scores, collections scores, fraud models, probability of defaults, enhancing the underwriting and risk performance for consumer lending solutions.
• Build predictive models, segmentation frameworks, and advanced experimentation methods, draw conclusions on the impact of product experiences, and communicate results to peers and leaders to measure and optimize execution of data-driven strategies in emerging areas of the consumer lending roadmap.
• Providing technical guidance to team to utilize models and develop advanced experimentation methods both traditional and new AI/ML techniques
• Partner closely with credit and fraud risk policy teams to influence roadmap and inferring results by monitoring the performance and effectiveness of strategies and maintain a thorough understanding of risk, operational, and product concepts to manage major portfolio risk factors.
• Create and manage multiple modeling scorecard initiatives and provide timely guidance and support for the growth and scalability of consumer lending products
• Drive strategic thinking and cross-functional alignment around data-driven approaches to optimizing the lending funnel.

Qualifications

• Have at least 10+ years of experience in data science, or a related field, with a strong background in leading complex, cross-functional projects and driving data-driven decision-making. Preferred experience in lending or fintech domain
• Demonstrate strategic foresight and are able to think beyond the immediate challenge. You can balance both short-term tactical priorities with long-term goals and strategically navigate competing demands on business growth in consumer lending space
• 5+ years of experience building, leading high-performing teams and capable of mentoring and growing a world-class data science team while working collaboratively with executives and other teams to drive company-wide impact.
• Have excellent communication skills and can clearly articulate complex data science concepts to non-technical stakeholders. You can effectively present insights, recommendations, and technical roadmaps to executives and other teams.
• Strong business and product sense: delight in shaping vague questions into well-defined analyses and success metrics that drive business decisions.
• Deep expertise in customer behavior analytics, predictive modeling, segmentation, and experimentation beyond A/B testing. Ability to build scalable, reusable analytics solutions; experience with AI tools is a plus.
• Domain experience in FinTech, Risk, Financial services, with specific expertise in Consumer Lending, is highly advantageous.
• PHD in quantitative fields such as Statistics, Mathematics, Computer Science, Machine Learning
• Create and manage multiple modeling scorecard initiatives for the improvement of business units.
• Develop market leading statistical models for the credit department producing risk scores, collections scores, fraud models, probability of conversion scores, affordability solutions, vetting income verification solutions and solutions to identify consumer vulnerability.
• Maintain a thorough understanding of risk, operational, and marketing concepts to manage major portfolio risk factors.
• Apply traditional model development techniques and approaches (e.g. linear & logistic regression) as well as adopting new AI/ML techniques such as gradient boosting, random forests, clustering, etc.
• Investigate escalated incidences in reports for further review to drive execution and business optimization.