Director, AI & Data Science
Position: Director, AI & Data Science
Location: Remote UK, US or India (4-6 hours overlap with east coast required)
About LRN
LRN is the world’s leading dedicated ethics and compliance SaaS company, helping more than 30 million people every year navigate complex regional and global regulatory environments and build ethical, responsible cultures. With over 3,000 clients across the US, EMEA, APAC, and Latin America—including some of the world’s most respected and successful brands—we’re proud to be the long-term partner trusted to reduce organizational risk and drive principled performance.
Named one of Inc Magazine’s 5000 Fastest-Growing Companies, LRN is redefining how organizations turn values into action. Our state-of-the-art platform combines intuitive design, mobile accessibility, robust analytics, and industry benchmarking—enabling organizations to create, manage, deliver, and audit ethics and compliance programs with confidence. Backed by a unique blend of technology, education, and expert advisement, LRN helps companies turn their values into real-world behaviors and leadership practices that deliver lasting competitive advantage.
About the role
The Director of AI & Data Science will define and lead the company’s applied AI strategy, delivering scalable ML and LLM-powered solutions that drive measurable business impact. This role partners closely with Product, Engineering, Security/Compliance, Sales, and Marketing to translate customer needs into production-ready AI capabilities.
This is a hands-on leadership role requiring both technical depth and organizational influence. You will coach and grow a small team, architect end-to-end AI systems, and help the company communicate AI value credibly and responsibly to customers and stakeholders.
Requirements
What you’ll do
- 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), with a strong focus on reliability and risk management
- 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 and product experiences; align with Security/Privacy/Legal for responsible use
- Enable GTM: support Sales and Marketing with AI narratives, customer-facing discussions, solutioning, and (when needed) technical validation
- Measure impact: define success metrics, run experiments, and communicate trade-offs and results clearly to execs and stakeholders
What we're looking for
- 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 and modern AI tooling, including prompt/system design, retrieval (RAG), fine-tuning (when appropriate), and evaluation
- Ability to translate ambiguous business problems into tractable AI work with clear scope, milestones, and measurable outcomes
- Excellent communication: can explain complex trade-offs to technical and non-technical audiences
- Experience collaborating with Sales/Marketing on customer-facing AI positioning and solutioning
- PhD Plus 2 years industry experience/ Masters plus 4 years / bachelor's plus 6 years industry experience
- Field of study computer science or at least 3 years as a software developer
- Strong Python and modern ML stack (e.g., PyTorch / sklearn; data tooling; experimentation, vector databases)
- Understanding of modern software development and technology practices like git, APIs, containerization, CI/CD
- Experience with model evaluation, guardrails, observability
- Understanding of responsible AI: privacy, security, governance, and risk controls—especially in regulated/enterprise contexts
- Node.js and other backend development skills
- Experience in B2B SaaS, compliance/ethics, risk, or enterprise workflows
- Experience designing evaluation frameworks for LLM features (quality, hallucination risk, latency/cost trade-offs)
- Prior customer-facing technical leadership (pre-sales, workshops, exec briefings)
Benefits
- Excellent medical benefits
- Paid Time Off (PTO) plus public holidays