Director of Data and AI

Overview

The Director of Data & AI is a hands-on, strategic leader responsible for evolving Origami Risk’s data and AI capabilities to drive innovation, scale, and business value. This person defines and delivers the unified vision for data architecture, AI enablement, and analytics strategy — ensuring our platform remains competitive, insightful, and compliant in a rapidly changing digital landscape. 

 

Starting base pay for this role is between $175,000 and $222,000. The actual base pay is dependent upon many factors, such as transferable skills, work experience, business needs, training, location, and market demands. The base pay range is subject to change and may be modified in the future. This role will be eligible for a bonus as well as competitive medical, dental, and vision benefits, wellness reimbursement, life insurance, and a 401(k) with company match. We offer vacation and sick leave benefits (under a flexible time off policy in most states).

 

 

Responsibilities

Strategic Leadership 

• Defines and owns the vision and roadmap for a unified data and AI strategy that supports business growth, customer insights, operational efficiency, and product innovation. 

• Conducts a full audit of the current data landscape (including sources, pipelines, and storage) to inform future-state architecture. 

• Leads the development of a cloud-native data lake house on AWS, leveraging Databricks for storage, transformation, and ML workloads. 

• Maintains a pulse on industry innovation and champion emerging technologies to position Origami as a leader in data and AI. 

Data Governance & Architecture 

• Improves data observability, lineage, quality, and governance practices to increase trust, reuse, and regulatory readiness. 

• Works cross-functionally to establish data policies, metadata standards, and stewardship practices aligned with internal and external compliance requirements. 

AI Strategy & Enablement 

• Drives the democratization of AI by embedding capabilities (e.g., summarization, predictive insights, workflow automation) across the platform for both end-users and administrators. 

• Establishes an internal AI enablement platform with APIs, tooling, and guardrails for standardized access to public LLMs and foundational models. 

• Oversees the prompt engineering and evaluation framework, including testing, versioning, and optimization across multiple user flows. 

• Develops a transparent AI usage and monetization model, including automated tracking for token usage, billing, and overages. 

Analytics & Adoption 

• Expands self-service analytics capabilities through ThoughtSpot, enabling teams and clients to generate insights from governed datasets. 

• Partners with Product, Engineering, and Product Marketing teams to achieve and track adoption of AI features, with a target of 50% usage across the customer base. 

• Works closely with internal stakeholders to identify value-driving metrics and communicate data and AI impact across the organization. 

Innovation & Commercialization 

• Identifies commercial opportunities across datasets and customer segments by developing data-driven and AI-enhanced product features. 

• Leads the acceleration of white-labeled AI capabilities, including assessments of external partnerships and vendors to expand feature sets. 

Communication & Leadership 

• Articulates the value of data and AI to executive leadership, internal stakeholders, and clients. 

• Manages and mentor a team of data engineers and ML/AI/Prompt engineers. This includes completing all team member review and compensation processes. 

• Collaborates closely with architects and product teams to embed data and AI capabilities into the platform’s core. 

Other Duties as Assigned

Qualifications

• Bachelor’s degree in Information Technology, Computer Science, Data Science, or a related field required. Master’s degree preferred. 

• 5+ years in a data and/or AI leadership role (Data Architecture, Data Engineering, Analytics, Machine Learning, etc.). 

• Experience in a SaaS B2B environment strongly preferred. 

• Prior experience commercializing data and AI into scalable, productized features. 

• Hands-on experience with: 

• AWS for data and compute infrastructure 

• Databricks for lake house architecture and ML workflows 

• ThoughtSpot or comparable analytics platforms