• 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.

• Define the data science vision and roadmap (what to build, why, and in what order). • Hire, mentor, and set standards for a high-performing Data team. • Own vendor delivery and coordination (external agency) until the internal team is established. • Partner closely with product, engineering, marketing, operations, fraud, and leadership; turn business goals into measurable outcomes. • Communicate complex model tradeoffs clearly to non-technical stakeholders. • Lead the applied AI/LLM direction for product experiences and internal tooling. • Build and maintain models for personalization/recommendations, engagement prediction, churn, and LTV/forecasting. • Own the full model lifecycle: training, evaluation, deployment, monitoring, and retraining. • Work with engineering to integrate models into backend services and client experiences with performance and scalability in mind. • Design and ship LLM-enabled capabilities (e.g., semantic search, assistants, automation, content generation, decision support). • Evaluate and adapt foundation models (fine-tuning/distillation/prompting) for product and internal workflows. • Develop anomaly detection and behavioral models to identify suspicious activity. • Create scoring and classification systems for patterns like manipulation, multi-accounting, and other abuse signals. • Partner with product/ops to design thresholds, rules, validation systems, and automated risk workflows. • Track emerging fraud trends and proactively address new attack vectors. • Define requirements for scalable pipelines supporting training, inference, experimentation, and monitoring. • Implement practical MLOps standards (CI/CD for models, versioning, monitoring, automated retraining). • Raise the bar for data quality, documentation, and metadata practices. • Build infrastructure patterns for LLM workloads (vector search, embeddings pipelines, evaluation, safety checks).

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking an experienced Contract Biostatistician to provide part-time statistical consulting support for a growing medical device company. This role will focus on the design, analysis, and interpretation of clinical studies involving human subjects, with an emphasis on diagnostic device performance and sensitivity testing. The ideal candidate has prior experience working with medical device clinical data and can operate independently in a fast-paced, resource-lean environment. - Provide statistical expertise for medical device studies involving human patients, including study design, analysis planning, and interpretation of results - Support diagnostic device studies, including evaluation of sensitivity, specificity, and other performance characteristics - Conduct and review sensitivity analyses to assess robustness of study results - Develop or review statistical analysis plans (SAPs) for device clinical studies - Analyze clinical and diagnostic data and summarize results for internal teams and external stakeholders - Collaborate with clinical, regulatory, and engineering teams to support regulatory submissions and study reporting - Provide ad hoc statistical consultation as needed Qualifications - Prior experience supporting medical device clinical studies involving human subjects - Hands-on experience with diagnostic studies, including sensitivity analyses - Strong knowledge of statistical methods relevant to medical device evaluation - Ability to work independently as a consultant and communicate findings clearly to non-statistical stakeholders Requirements - Master’s degree in Statistics, Biostatistics, or a related field - Experience supporting studies intended for regulatory submission (e.g., FDA, notified bodies) - Familiarity with common statistical software (e.g., SAS, R, or equivalent)