Head of Data Platform & AI

About the Company:

Octave is a modern behavioral health practice creating a new standard for care delivery that’s both high-quality and accessible. With in-person and virtual clinics in multiple states, the company offers evidence-based individual, couples, and family therapy, while pioneering relationships with payers to make care more affordable through insurance. By raising the bar on how care is delivered and how providers are supported, we are building a sustainable system that values equity, affordability, and effectiveness.

Job Summary:

Octave is looking for a Head of Data Platform & AI to lead the technical vision and execution across our data platform and emerging AI, machine learning, and Data Science capabilities. This role will own and evolve Octave’s data and AI/ML technical foundation, set the technical/strategic direction for AI/ML in close partnership with Product and cross-functional business leaders, and build the team required to execute that vision as Octave grows.

This is a leadership role for a builder. While you will set strategy, lead the team, and operate as a senior cross-functional partner, you will remain close to the technology and be expected to contribute directly to architecture, prototypes, technical evaluation, and difficult engineering problems. You will initially lead a Data Platform team and work closely with Product, Engineering, Analytics, and business leaders across Octave.

Management Responsibilities:

• Develops, coordinates, and implements  systems, policies, procedures, and productivity standards.
• Provide leadership and guidance to the team, ensuring the achievement of goals and objectives.
• Foster a positive and collaborative work environment.
• Oversee the planning, execution, and completion of projects and initiatives within the team.
• Establish and monitor operational processes and workflows to enhance efficiency and productivity.
• Streamline operational processes, enhance efficiency, and improve overall organizational performance. 
• Implement best practices, monitor key performance indicators (KPIs), and develop strategies to achieve operational excellence.
• Ensures a safe, secure, and compliant work environment.
• Accomplishes staff results by communicating job expectations; planning, monitoring, and appraising job results.
• Build and manage a high-performing team, including hiring, training, and development.
• Provide leadership to the team, including setting goals, providing guidance, and ensuring the team's overall success.
• Set performance expectations and goals for team members. Conduct regular performance evaluations, provide feedback, and address performance issues. 
• Identify skill gaps within the team and develop strategies for filling those gaps. Support employee development through training, mentoring, and coaching. Identify high-potential employees and create succession plans.

Duties & Responsibilities: 

• Define Octave’s Data & AI Technical Vision
• Develop and own the technical strategy and roadmap for Octave’s data platform and AI/ML capabilities, while shaping company-level direction and investment priorities by bringing a data and AI perspective to decisions about where Octave competes and how it grows.
• Own company-level KPIs spanning Octave's AI/ML and Data Platform areas — defining the measures that matter, holding accountability for outcomes against them, and reporting on progress to the executive team.
• Partner closely with Product and cross-functional leaders to identify where AI, machine learning, and Data Science can create meaningful customer and business value, and own the technical strategy and capabilities required to bring those opportunities to life.
• Serve as Octave’s senior technical leader and thought partner on data, AI, ML, and Data Science.
• Help Product, Engineering, and business leaders evaluate opportunities, understand technical feasibility and tradeoffs, and shape Octave’s approach to emerging capabilities.
• Develop a clear point of view on where traditional machine learning, predictive modeling, generative AI, LLM-based systems, and other approaches are appropriate.
• Establish principles for when Octave should build capabilities internally, leverage third-party platforms or models, or partner externally.
• Balance near-term delivery with the foundational investments required to support Octave’s longer-term strategy.  
• Lead AI/ML and Data Science Technical Strategy
• Establish the technical capabilities required to develop, evaluate, deploy, monitor, and continuously improve production AI and machine-learning systems.
• Work with Product and business leaders to translate promising AI/ML opportunities into well-defined technical initiatives.
• Ensure AI/ML initiatives are grounded in clear customer or business outcomes rather than pursued as standalone technology projects.
• Guide technical experimentation and prototyping to validate opportunities before making significant investments.
• Establish rigorous approaches to model and system evaluation, experimentation, observability, feedback loops, governance, privacy, security, and responsible AI.
• Determine the Data Science, ML Engineering, and Applied AI capabilities Octave needs over time and develop an appropriate organizational model to support them.  
• Own and Evolve the Data Platform
• Own the architecture and evolution of Octave’s centralized data platform and the systems that collect, transform, store, govern, and serve data across the company.
• Ensure the platform is reliable, scalable, observable, secure, and straightforward for downstream consumers to use.
• Establish strong engineering practices around data quality, testing, lineage, documentation, monitoring, and reliability.
• Build scalable interfaces and data products that enable analysts, Product, Engineering, and other teams to work effectively with trusted data.
• Make pragmatic architectural and technology decisions that balance scalability and technical quality with the speed required in a fast-growing company.
• Avoid unnecessary complexity and ensure platform investments are justified by real customer, product, or business needs.  
• Enable a Decentralized Analytics Organization
• Treat analysts embedded within Octave’s product and business organizations as key customers and partners of the Data Platform.
• Partner with analysts and their leaders to understand common needs, remove technical bottlenecks, and improve access to reliable and well-modeled data.
• Establish shared technical standards, platform capabilities, and ownership models that allow decentralized analytics teams to move quickly without creating unnecessary fragmentation.
• Create clear ownership boundaries between the centralized Data Platform organization and embedded analytics teams.
• Build foundational data capabilities that allow analysts to spend more time generating insights and less time solving recurring infrastructure or data-quality problems.  
• Build the Capabilities and Team Octave Needs
• Lead, mentor, and develop the existing Data Platform team while remaining close to the technical work.
• Continuously assess the technical capabilities required to execute Octave’s evolving data and AI strategy.
• Develop existing team members where appropriate and recruit exceptional external talent when specialized skills or experience are missing internally.
• Proactively identify capability gaps rather than scaling the organization through headcount alone.
• Build additional specialized capabilities—including Data Science, ML Engineering, Applied AI, or other disciplines—as product and business needs warrant.
• Determine when capabilities should live centrally, be embedded within Product or Engineering teams, or operate through another organizational model.
• Develop strong technical leaders and individual contributors and create an environment characterized by high standards, ownership, curiosity, collaboration, and pragmatic execution.
• Build an organization that can scale without introducing unnecessary layers or process.  
• Remain a Hands-On Technical Leader
• Stay sufficiently close to the technology to provide credible technical leadership and make high-quality architecture and engineering decisions.
• Dive into system design, architecture, data modeling, prototypes, code, technical evaluations, or production issues when your involvement can materially accelerate the team.
• Personally engage with emerging AI/ML technologies enough to de