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
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Manage the complete lifecycle of initiatives from requirement analysis through implementation, evaluation, and continuous improvement.
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Analyse business, product, and learner data to identify opportunities for improving performance, user experience, and operational efficiency.
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Design, develop, and optimise machine learning models, analytical solutions, and automations to address business needs.
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Identify opportunities to leverage emerging AI capabilities and intelligent automation to improve products, processes, and business outcomes.
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Develop reliable data pipelines, reports, dashboards, and scalable analytical solutions that support operational and strategic decision-making.
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Deploy, monitor, and maintain production solutions, ensuring reliability, scalability, security, and operational performance.
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Ensure the accuracy, integrity, and quality of data, analyses, models, and all project deliverables while adhering to data governance standards.
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Evaluate the performance of implemented solutions and continuously refine models, methodologies, and workflows to maximise business value.
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Research, experiment with, and evaluate emerging technologies, AI frameworks, and industry best practices to enhance solution capability and innovation.
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Collaborate with cross-functional teams to translate business requirements into scalable, data-driven solutions and promote the adoption of AI-driven approaches where appropriate.
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Develop robust and maintainable solutions by following engineering best practices for code quality, testing, documentation, and version control.
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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
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Bachelor's or Master's degree in Data Science, Computer Science, Artificial Intelligence, Statistics, Mathematics, or a related quantitative discipline.
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2–4 years of hands-on experience in data science, machine learning, predictive analytics, or artificial intelligence.
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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.
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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.
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Demonstrates ownership, continuous learning, and the ability to evaluate and adopt emerging AI technologies to improve business outcomes.
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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.
