Data Scientist - L2 Role Objective Develop scalable analytical and AI-driven solutions that transform business challenges into actionable insights, enabling data-informed decision-making and continuous improvement. Roles & Responsibilities • Manage the complete lifecycle of initiatives from requirement analysis through implementation, evaluation, and continuous improvement. • Analyse business, product, and learner data to identify opportunities for improving performance, user experience, and operational efficiency. • Design, develop, and optimise machine learning models, analytical solutions, and automations to address business needs. • Identify opportunities to leverage emerging AI capabilities and intelligent automation to improve products, processes, and business outcomes. • Develop reliable data pipelines, reports, dashboards, and scalable analytical solutions that support operational and strategic decision-making. • Deploy, monitor, and maintain production solutions, ensuring reliability, scalability, security, and operational performance. • Ensure the accuracy, integrity, and quality of data, analyses, models, and all project deliverables while adhering to data governance standards. • Evaluate the performance of implemented solutions and continuously refine models, methodologies, and workflows to maximise business value. • Research, experiment with, and evaluate emerging technologies, AI frameworks, and industry best practices to enhance solution capability and innovation. • Collaborate with cross-functional teams to translate business requirements into scalable, data-driven solutions and promote the adoption of AI-driven approaches where appropriate. • Develop robust and maintainable solutions by following engineering best practices for code quality, testing, documentation, and version control. • Demonstrate ownership, accountability, and a continuous improvement mindset by delivering high-quality outcomes and contributing to the organisation's AI and data capabilities. Skills & Qualifications • Bachelor's or Master's degree in Data Science, Computer Science, Artificial Intelligence, Statistics, Mathematics, or a related quantitative discipline. • 2–4 years of hands-on experience in data science, machine learning, predictive analytics, or artificial intelligence. • Strong proficiency in Python, SQL, and data visualization tools, with experience in building scalable analytical solutions.ands-on experience in developing, fine-tuning, evaluating, and deploying Machine Learning (ML) models for real-world business applications. • Working knowledge of Generative AI, Large Language Models (LLMs), Natural Language Processing (NLP), embedding models, prompt engineering, and Retrieval-Augmented Generation (RAG) frameworks is preferred. • Experience with model deployment, MLOps practices, APIs, version control (Git), cloud platforms, and production monitoring. • Strong analytical and problem-solving skills with the ability to translate business requirements into practical AI and data-driven solutions. • Demonstrates ownership, continuous learning, and the ability to evaluate and adopt emerging AI technologies to improve business outcomes. • Good to have : Understanding of gamification concepts and mechanics (points, levels, badges, streaks, leaderboards, feedback loops, branching/scenario logic), and how they translate into measurable engagement or behavioral data signals.