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Octave Health

In-network therapy for individuals, couples, families

Head of Data Platform & AI

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeRemote in USA$239.3k - $280k

About the Job

Requirements

  • Strong technical foundation and prior experience as a highly capable individual contributor before moving into leadership.
  • Strong understanding of traditional machine-learning and Data Science approaches and newer AI capabilities, including LLM-based systems and generative AI.
  • Strong judgment around build-versus-buy decisions and the ability to assess rapidly evolving technologies pragmatically.
  • Excellent communication skills and the ability to move fluidly between detailed technical discussions and conversations with senior business and product leaders.
  • Comfort operating in a small, fast-moving organization where senior leaders remain close to execution.
  • Use LLMs and agentic tools in day-to-day work as practical tools for accelerating execution, with sound judgment about where they create leverage and where they get in the way.
  • At least 8 years of experience building and leading data platform, data engineering, Data Science, ML, or AI capabilities in high-growth technology environments.
  • Demonstrated experience architecting and building modern data platforms and production data systems.
  • Meaningful experience applying machine learning and/or AI to real-world product or business problems and taking solutions from experimentation into production.
  • Experience partnering closely with Product teams to identify and deliver technically sophisticated product capabilities.
  • Demonstrated ability to diagnose organizational capability gaps, build new technical capabilities, and recruit senior, specialized technical talent to fill them.

Responsibilities

  • 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, define the measures that matter, hold accountability for outcomes against them, and report progress to the executive team.
  • Partner with Product and cross-functional leaders to identify where AI, machine learning, and Data Science can create customer and business value, and own the technical strategy and capabilities required to deliver those opportunities.
  • 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 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, use third-party platforms or models, or partner externally.
  • Balance near-term delivery with foundational investments needed to support Octave’s longer-term strategy.
  • Establish technical capabilities 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 grounded in customer or business outcomes.
  • Guide technical experimentation and prototyping to validate opportunities before significant investments are made.
  • 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 organizational model to support them.
  • Own the architecture and evolution of Octave’s centralized data platform and systems that collect, transform, store, govern, and serve company data.
  • Ensure the platform is reliable, scalable, observable, secure, and straightforward for downstream consumers to use.
  • Establish engineering practices for 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 balancing scalability and technical quality with the speed required in a fast-growing company.
  • Ensure platform investments are justified by customer, product, or business needs and avoid unnecessary complexity.
  • Treat analysts embedded within 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, well-modeled data.
  • Establish shared technical standards, platform capabilities, and ownership models that let decentralized analytics teams move quickly without unnecessary fragmentation.
  • Create clear ownership boundaries between the centralized Data Platform organization and embedded analytics teams.
  • Build foundational data capabilities that allow analysts to focus more on generating insights and less on recurring infrastructure or data-quality problems.
  • 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 external talent when specialized skills or experience are missing internally.
  • Identify capability gaps proactively rather than scaling the organization through headcount alone.
  • Build specialized capabilities—including Data Science, ML Engineering, Applied AI, or other disciplines—as product and business needs warrant.
  • Determine when capabilities should be centralized, embedded within Product or Engineering teams, or organized through another model.
  • Develop 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 unnecessary layers or process.
  • Develop, coordinate, and implement systems, policies, procedures, and productivity standards.
  • Provide leadership and guidance to the team to ensure achievement of goals and objectives.
  • Foster a positive and collaborative work environment.
  • Oversee the planning, execution, and completion of team projects and initiatives.
  • Establish and monitor operational processes and workflows to enhance efficiency and productivity.
  • Streamline operational processes, enhance efficiency, and improve organizational performance.
  • Implement best practices, monitor key performance indicators, and develop strategies for operational excellence.
  • Ensure a safe, secure, and compliant work environment.
  • Communicate job expectations; plan, monitor, and appraise staff results.
  • Build and manage a high-performing team, including hiring, training, and development.
  • Set team goals, provide guidance, and ensure the team’s overall success.
  • Set performance expectations and goals for team members, conduct regular performance evaluations, provide feedback, and address performance issues.
  • Identify team skill gaps and develop strategies to fill them; support employee development through training, mentoring, and coaching; identify high-potential employees and create succession plans.
  • Raise the bar for the team’s use of LLM and agentic tools to improve engineering productivity, accelerate experimentation, and increase team leverage.
  • Stay sufficiently close to the technology to provide credible technical leadership and make high-quality architecture and engineering decisions.
  • Contribute directly to system design, architecture, data modeling, prototypes, code, technical evaluations, or production issues when involvement can materially accelerate the team.
  • Engage with emerging AI/ML technologies to develop an informed view of their capabilities, limitations, and relevance to Octave.
  • Serve as a technical thought partner to Data Platform engineers, Product engineers, analysts, and senior company leadership.
  • Set a high technical bar while empowering the team to own execution.
  • Use technical depth to simplify problems and help the company avoid underinvestment and unnecessary overengineering.
  • Sit at a desk and work on a computer for prolonged periods.
  • Communicate frequently through virtual meeting applications such as Zoom and Google Meet.
  • Observe and communicate information on a company-provided laptop.
  • Move up to 10 pounds on occasion.

About the Company

Octave Health

In-network therapy for individuals, couples, families

Octave Health provides personalized therapy services for individuals, couples, and families. Therapy is tailored to each client’s needs using evidence-based approaches, with therapists matched to clients based on expertise and compatibility to foster real human connection. The service is delivered in-network with several insurance providers, billing the client’s insurance to improve accessibility. The company reports outcomes such as a 37% reduction in depression symptoms over 12 weeks. Octave’s goal is to improve mental health outcomes by delivering evidence-based, closely matched therapy that is affordable and accessible through insurance coverage.

Company size
501-1,000
Company stage
Late Stage VC
Total funding
$125.3M
Headquarters
San Francisco, CA
Founded
2018

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