• The Lead Data Scientist is responsible for leading data science initiatives that drive business profitability, increased efficiencies and improved customer experience. • This role assists in the development of the Home Depot advanced analytics infrastructure that informs decision making by applying expertise of both business and Advanced Analytics Modeling techniques. • Lead Data Scientists focus on seeking out business opportunities to leverage data science as a competitive advantage. • This role has expertise in one or more data science specializations, such as optimization, computer vision, recommendation, search or NLP. • Responsible for large data science projects, identifying opportunities to leverage best technology and approach, and mentoring data scientists on the project team. • Expected to own the library of reusable algorithms for future use, ensuring developed codes are documented. • Supports the building of skilled and talented data science teams by providing input to staffing needs and participating in the recruiting and hiring process. • Leads data science communities across several business units.

• Lead the design and development of ML systems that solve complex, ambiguous business problems • Make sound technical decisions on model architecture, evaluation methodology, and tradeoffs • Set standards for model validation, testing, and monitoring across the team • Identify when "good enough" is appropriate vs. when deeper investment is warranted • Debug and troubleshoot models that fail in production - understand why they fail, not just that they fail • Frame business problems as well-defined ML tasks with clear success criteria • Build robust predictive models (classification, regression, time series, causal inference) • Implement rigorous train/validation/test methodology to ensure real-world generalization • Identify and prevent data leakage, overfitting, and other failure modes before they reach production • Define metrics that align model performance with actual business outcomes • Conduct holdout testing on true out-of-sample data - recognize when CV metrics are misleading • Design and analyze experiments to measure causal impact • Communicate model limitations, uncertainty, and risk to technical and non-technical stakeholders • Partner with product, engineering, and business teams to ensure ML solutions solve real problems • Translate complex technical concepts into actionable recommendations for stakeholders • Contribute to hiring and technical interviews

• Own the data strategy, architecture, and intelligence capabilities for Bizee. • Design the data structures that power customer intelligence, marketing analytics, jurisdictional compliance, and financial reporting. • Lead a team of approximately 3-4 Technical Leads, Principal Data Scientists, Analysts, and Engineers. • Partner directly with Platform Engineering, Product, and Marketing leadership. • Ensure the data foundation enables subscription transformation, lifecycle marketing, and AI-powered product experiences. • Use AI tools to accelerate work and team processes. • Design data systems with AI consumption as a requirement.

About Bizee Bizee (formerly Incfile) has helped over 1 million entrepreneurs start and run their businesses. We’re transforming from a transaction-focused formation business to an AI-powered operating system for entrepreneurs. We champion the everyday entrepreneur. We believe in self-determination, grit, and earned success. No fluff, no jargon, no pretense. We work hard, ship fast, and let our products speak for themselves. We’re looking for a data leader who builds. Someone who can design the data architecture that powers intelligent products, stand up the analytics that drive business decisions, and lead a team that turns raw data into competitive advantage. If you believe data is the foundation of AI, not an afterthought, this role is for you. What Makes Working Here Different - Data Is the Foundation of Everything: AI-powered products are only as good as the data underneath them. You’re not building dashboards for a data warehouse; you’re designing the intelligence layer that powers an AI operating system for entrepreneurs. - Greenfield Data Strategy: We’re standing up master data management, customer intelligence, marketing analytics, and 50-state jurisdictional data models. You define the strategy and build the foundation. - Business-Critical Domains: Customer data, jurisdictional compliance data, marketing attribution, and financial data. These aren’t academic exercises. Getting the data model right directly impacts revenue, compliance, and company valuation. - AI-Native From Day One: Every data system you build should be designed with AI consumption in mind. You’re not just making data queryable; you’re making it intelligent. The Role As Head of Data & Analytics, you own the data strategy, architecture, and intelligence capabilities for Bizee. You will design the data structures that power customer intelligence, marketing analytics, jurisdictional compliance, and financial reporting across the platform. This is not a governance or oversight role. You are a hands-on data leader who personally designs data models, reviews pipeline architecture, and writes queries to validate business logic. You lead a team of approximately 3-4 Technical Leads, Principal Data Scientists, Analysts, and Engineers providing overall leadership to 15-20 team members. You will be spending meaningful time in the work yourself. We call this a player-coach: you set the vision and strategy, then roll up your sleeves and build alongside your team. You will partner directly with Platform Engineering, Product, and Marketing leadership to ensure the data foundation enables their roadmaps. You own the data architecture decisions that determine whether the company can execute on subscription transformation, lifecycle marketing, customer intelligence, and AI-powered product experiences. This means you need deep business domain expertise. You understand how data models map to business outcomes, not just technical schemas. This is an AI-native leadership role. You design data systems with AI consumption as a first-class requirement. You use AI tools to accelerate your own work and your team’s work. You have a point of view on how modern data architecture should evolve to support AI-powered products and decision-making. Reports to CTO. Data Domains You’ll Own - Customer Intelligence: Unified customer data model spanning acquisition, formation, subscription lifecycle, renewals, and churn. The foundation for personalization, segmentation, and AI-driven lifecycle management. - Jurisdictional & Compliance Data: 50-state regulatory requirements, filing rules, fee structures, and compliance timelines. This is a uniquely complex domain that directly impacts product accuracy and customer trust. - Marketing Analytics & Attribution: Marketing mix modeling, full-funnel experimentation, channel attribution, CAC/LTV analysis, and spend optimization. Partnering directly with Marketing leadership to connect data to growth decisions. - Financial & Operational Data: Revenue analytics, subscription metrics, NetSuite data integration, and financial reporting foundations that support investor-grade visibility. - Master Data Management: Defining the canonical data models and governance standards that ensure consistency across all systems. You can’t build automation on unreliable data, and you own making the data reliable. What You’ll Deliver - Data Strategy & Roadmap: Deliver a comprehensive data strategy aligned to business priorities (subscription transformation, marketing efficiency, customer intelligence). A roadmap that Platform, Product, and Marketing leadership can plan against. - Master Data Foundation: Design and begin implementing canonical data models for Customer, Product, Jurisdiction, and Transaction domains. Establish Master Data Records with clear definitions, reconciliation rules, quality standards, and governance that the entire organization trusts. - Data Synchronization: Ensure reliable, governed data flow across CMS (Statamic), NetSuite, payment processors (Adyen), state filing systems, and customer-facing applications. Establish clear ownership, SLAs, and reconciliation processes for cross-system data integrity. - Marketing Analytics Capability: Stand up marketing mix modeling, attribution, and experimentation infrastructure. Give Marketing leadership the data visibility they need to optimize spend and prove ROI. - Customer Intelligence Platform: Build the unified customer view that enables lifecycle segmentation, churn prediction, and personalized engagement. This is the data backbone for our subscription transformation. - Self-Service Analytics: Enable Marketing, Product, and Operations teams to answer their own data questions without engineering tickets. Reduce ad-hoc data request volume by 60%+. - AI-Ready Data Layer: Design data pipelines and storage patterns that serve AI/ML workloads as a first-class use case. Make it easy for product teams to build intelligent features on top of clean, well-modeled data. - Team Build-Out: Assess current team capabilities, define the target org structure, and hire or develop to fill gaps. Build a team that operates with clear domain ownership and cross-functional partnership.