Data Analytics Lecturer

Role Overview

Creative teams are being asked to do more with fewer resources, and this role exists to make AI work for them. You'll bridge the gap between what machine learning tools can do and how designers, writers, and producers actually work — by identifying bottlenecks, running experiments, and scaling what works. The employer is banking on you to turn hype into production-ready workflows that improve output without sacrificing craft.

Key Responsibilities

• Audit current creative workflows and identify where AI/ML automation could remove repetitive tasks or speed up iteration.
• Design and maintain a library of prompts, templates, and micro-automations for text, image, and video generation tools.
• Partner with project managers and department leads to run pilot programs and measure the impact on delivery times and quality.
• Deliver hands-on training sessions to help artists and editors adopt AI tools confidently rather than seeing them as a threat.
• Build governance guidelines around AI usage, including quality checks, copyright considerations, and brand safety.
• Evaluate new AI platforms and develop buy versus build recommendations, presenting cost-benefit analyses to leadership.
• Document transformation playbooks and share learnings so successful experiments can be replicated across teams.

Requirements & Qualifications

• 3+ years of experience leading or consulting on digital transformation projects, ideally within a creative, media, or agency environment.
• Hands-on proficiency with AI/ML tools such as ChatGPT, Midjourney, Stable Diffusion, Runway, or equivalent, including experience with prompt engineering and iterative refinement.
• Familiarity with no-code/low-code automation platforms (e.g., Zapier, Make) or scripting languages like Python for connecting AI tools to existing workflows.
• A clear point of view on how AI is transforming creative industries, backed by examples of either personal projects or case studies you have executed.
• Strong stakeholder management skills and the ability to translate technical capabilities into business language for non-technical teams.
• A bias toward experimentation and measurement, with experience defining success metrics for innovation initiatives.
• Bachelor's degree in a relevant field (e.g., computer science, design, business, or communications) or equivalent practical experience.