Oyster®
Senior Director, Data Platform and AI
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The Role
While this position is posted in a specific location, all of Oyster’s positions are fully remote and you can work from home. Forever. To create the best experience for our new hire, this role requires you to be based within +3 / -7 UTC.
As the Senior Director, Data Platform and AI, you will own the technical infrastructure and strategic direction that transforms Oyster into an AI-native global platform, decoupling scaling overhead from platform usage growth. Part of our Senior Tech Leadership Team, this highly specialized leadership role unites data platforms, product analytics pipelines, knowledge management, and advanced automation layer frameworks.
You will lead the effort to transition AI initiatives from isolated experimentation into centralized systems. This is an organizational transformation role as much as a technical one. You will leverage AI to optimize our internal workflows, modernize our knowledge architecture, and directly enhance our customer-facing product. By building unified data structures and connectivity pipelines, you will directly lower our cost-to-serve and equip our global teams with the clear data and context they need to operate efficiently. Crucially, you will also educate and empower teams across Oyster to scale their own AI use productively.
Key Responsibilities
- Drive the company-wide AI agenda: scaling local AI initiatives into centralized production systems that create measurable business value.
- Own broad, high-impact AI initiatives that have cross functional impact: Moving our AI usage from past narrow or siloed automation examples to execute a company operational transformation.
- Partner with Product, Engineering, and operational leaders to embed AI capabilities into our customer-facing platform, internal business workflows, and core processes.
- Serve as the ultimate technical authority for our data and AI infrastructure, making critical architectural decisions across MLOps pipelines, LLM orchestration frameworks, and distributed data systems.
- Oversee the development of a scalable corporate data platform that serves as the foundational bedrock for all AI and machine learning capabilities.
- Transform our internal knowledge base by building the systemic data architecture needed to turn unstructured information into intelligent, easily searchable, and actionable assets.
- Re-build and evolve our company-wide data structures and connectivity pipelines to be optimized to deploy, run, and maintain AI models efficiently while lowering cost-to-serve.
- Set the company wide tooling standards, maintain technical quality guardrails, and establish engineering best practices for embedded AI specialists working within distinct business units.
- Actively support the implementation of key local solutions built by decentralized specialists, ensuring they have the tools and centralized platform support required to succeed.
- Maintain high levels of ethical compliance by ensuring all global AI initiatives strictly adhere to data privacy, platform security, and ethical compliance standards.
- Educate and empower teams across the organization to scale their own AI use productively, providing them with the structural frameworks needed to safely build and innovate within their functions.
- Partner deeply with the People and Operations functions to champion organizational AI capability, replacing operational uncertainty with structured technical training loops that convert manual specialists into power-users of automated solutions.
- Drive the organizational change management and literacy efforts required to reshape daily workflows, shifting company culture and habits from traditional manual processes to AI-assisted operations.
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Core Requirements
- A genuine passion for artificial intelligence that goes far beyond product feature delivery. You possess a demonstrated interest in, and concrete evidence of driving, internal organizational change, specifically regarding how AI reshapes day-to-day employee workflows and habits.
- Proven experience operating within a service-delivery model or a highly complex operational business, where data strategy directly impacts intricate, real-world workflows and diverse stakeholder ecosystems.
- A history weighted heavily toward data engineering, distributed systems development, platform services infrastructure, or system architecture over front-end application product design.
- A proven deep technical track record in distributed data systems, MLOps pipelines, and LLM orchestration. You can easily evaluate complex models and emerging technologies, serving as the ultimate technical authority for our infrastructure.
- Documented success building, testing, and scaling complex asynchronous data structures, machine learning routing layers, or high-volume API integrations within modern software platforms.
- The ability to view the organization holistically. You use your understanding of how interconnected technical platforms, business processes, and human teams interact, designing data and AI solutions that optimize entire workstreams rather than individual silos.
- A proven track record of driving internal adoption for major technology or workflow shifts. You can break down complex AI frameworks for non-technical teams, guide employees through operational transitions, and successfully shift daily habits from manual work to AI-assisted processes.
- Comprehensive familiarity with model validation systems, technical anomaly logging, data pipeline health metrics, and automated governance frameworks (e.g., custom logging models or open-source pipeline validation stacks).
- BONUS: Background architecting semantic search solutions, internal retrieval-augmented generation engine layers, or multi-tenant customer data structures from zero-to-one phases.
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You'll also need
- A reliable home internet connection and fluency in both written and spoken English.
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