Director, AI & Data Science
New
Remote UK, US or India, 4-6 hours overlap with east coast requiredFull-TimeDirector
Salary not disclosed
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Job Details
- Experience
- PhD Plus 2 years industry experience/ Masters plus 4 years / bachelor's plus 6 years industry experience
- Required Skills
- Node.jsPythonMachine LearningPyTorchData scienceLLMMLOps
Requirements
- Proven ability to lead an applied AI team (player-coach) and drive delivery in a production environment
- Strong end-to-end ML/AI engineering judgment: data, modeling, evaluation, deployment, monitoring, iteration
- Practical experience with LLMs, prompt/system design, retrieval (RAG), fine-tuning, and evaluation
- Ability to translate ambiguous business problems into tractable AI work
- Excellent communication: can explain complex trade-offs to technical and non-technical audiences
- Experience collaborating with Sales/Marketing on customer-facing AI positioning
- Strong Python and modern ML stack (e.g., PyTorch, sklearn, vector databases)
- Understanding of modern software development practices (git, APIs, containerization, CI/CD)
- Experience with model evaluation, guardrails, and observability
- Understanding of responsible AI, privacy, security, and governance
- Node.js and backend development skills
- Experience in B2B SaaS, compliance/ethics, risk, or enterprise workflows
- Experience designing evaluation frameworks for LLM features
Responsibilities
- Lead and grow the AI & DS function: manage/mentor a team of ML/data scientists and guide hiring, prioritization, and delivery
- Set technical strategy and standards: choose architecture, tooling, evaluation methods, and deployment patterns (MLOps / LLMOps)
- Build and ship AI capabilities: deliver end-to-end solutions (problem framing → data → modeling → evaluation → deployment → monitoring)
- Partner cross-functionally: collaborate with Product/Engineering to integrate AI into workflows
- Align with Security/Privacy/Legal for responsible AI use
- Enable GTM: support Sales and Marketing with AI narratives, customer-facing discussions, and solutioning
- Measure impact: define success metrics, run experiments, and communicate trade-offs and results to execs
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