Data Scientist- L2

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. 24 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. .