Artificial Intelligence Data Specialist

Overview

In this position, you’ll be tasked with finding innovative ways to use data-driven artificial intelligence (AI) to streamline business operations and improve decision-making. This means working with large, complex, diverse datasets that traditional methods can’t handle effectively. Your role involves advocating for AI within your company, introducing new tools and technologies, and ensuring all initiatives adhere to ethical guidelines and data governance principles. By leveraging data, machine learning, and AI, you’ll help improve products and processes internally and in the industry. Throughout the apprenticeship, you’ll take on new projects in a flexible environment, maintain technical standards for AI solutions, and lead research efforts to explore AI’s potential applications within the company.

What You’ll Do

• Duty 1: Lead new projects in a flexible environment and uphold technical standards for AI solutions used by the organisation and its clients. Research prototypes are developed according to industry norms and organisational needs.
• Duty 2: Analyse and summarise research findings in AI and related fields, making them applicable to the organisation. Translate these findings into potential impacts, opportunities, and risks.
• Duty 3: Use research outcomes to create innovative, scalable AI solutions for business challenges. Ensure projects align with industry standards and meet organisational needs.
• Duty 4: Contribute to AI systems’ ethical and legal development, ensuring they comply with organisational and regulatory requirements. Deliver solutions responsibly and establish governance frameworks considering privacy issues.
• Duty 5: Design efficient architectures to maximise the impact of AI systems for the organisation. Ensure these architectures meet organisational needs within agreed timelines.
• Duty 6: Develop creative approaches to solving business problems using AI and related technologies. Ensure solutions meet industry standards and can be integrated into existing business systems.
• Duty 7: Design scalable analytical solutions using AI and related technologies for business problems. Ensure solutions meet industry standards and organisational timelines.
• Duty 8: Promote using AI tools and technologies within the organisation. Advocate for their adoption and stay updated on new developments.
• Duty 9: Develop robust data systems to support the organisation, integrating new data sources and applying analytics. Ensure compliance with organisational and architectural best practices.
• Duty 10: Create and maintain technical roadmaps for data management, ensuring they meet organisational needs and include support and business processes.
• Duty 11: Develop customised mechanisms for accessing and analysing complex datasets to achieve business outcomes. Ensure these mechanisms meet organisational requirements and performance standards.
• Duty 12: Identify best practices in AI data systems and provide technical oversight to meet business objectives. Follow scientific methodology and communicate uncertainties in research findings.
• Duty 13: Evaluate risks and biases associated with AI applications in business contexts according to organisational policies and industry standards.
• Duty 14: Provide technical guidance to the organisation on emerging AI opportunities. Identify strategic opportunities and generate insights relevant to business goals.
• Duty 15: Continuously learn about technological advancements to enhance skills and take responsibility for professional development.

What You’ll Learn

• Using AI and Machine Learning for Business Goals: Learn how to use AI and machine learning techniques like data mining and natural language processing to achieve business objectives.
• Maximising Impact with Data and Technology: Understand how to use modern data storage and processing technologies and machine learning to draw conclusions from research and benefit the organisation.
• Applying Statistical Methods to Projects: Apply advanced statistical and mathematical methods to real-world business projects.
• Data Extraction and Integration: Learn to extract and link data from various systems to meet business needs.
• Effective Data Analysis and Research Design: Design and deploy data analysis techniques to effectively fulfil business and customer requirements.
• Delivering Data Products for Business Solutions: Deliver data products that engage customers and solve business problems using different development and project management methods.
• Problem Solving and Software Evaluation: Learn to solve problems and evaluate software solutions through various testing methods.
• Interpreting Organisational Policies in AI and Data: Understand how organisational policies relate to AI and data practices.
• Legal, Ethical, and Regulatory Frameworks: Learn about legal, ethical, and regulatory frameworks affecting data product development and delivery.
• Aligning Role with Organisational Objectives: Understand how your role supports organisational strategy and objectives.
• Impact of AI and Data Science: Explore the roles and impact of AI, data science, and data engineering in industry and society.
• Ethical Considerations in Technology: Consider AI and data technologies’ wider social context and ethical implications.
• Translating Theory into Practice: Understand the compromises and trade-offs in applying theoretical concepts in real-world scenarios.
• Business Value of Data Products: Recognise the business value of data products that meet quality standards and deadlines.
• Engineering Principles in Data Product Development: Understand engineering principles in designing, developing, and deploying data products.
• High-Performance Computing: Learn about high-performance computer architectures and their effective utilisation.
• Industry Trends in AI and Data Science: Stay updated on current trends and apply them effectively.
• Programming for Data Engineering: Learn programming languages and techniques relevant to data engineering.
• Understanding Statistical and Machine Learning Methods: Grasp the principles and properties behind statistical and machine learning methods.
• Data Collection, Storage, Analysis, and Visualisation: Understand the processes involved in collecting, storing, analysing, and visualising data.
• Collaboration with Team Members: Learn how AI and data science techniques support and enhance teamwork.
• Mathematical Principles in Organisational Context: Understand the relationship between mathematical principles and core AI and data science techniques within an organisational setting.
• Model Validation in AI Projects: Identify and utilise different performance and accuracy metrics for model validation.
• Error and Bias Awareness: Recognise sources of error and bias in data, including their impact on choice of dataset and methodologies.
• Programming for Scientific Analysis: Learn programming languages and machine learning libraries for scientific analysis and simulation.
• Application of Scientific Method: Apply the scientific method in research and business contexts, including experiment design and hypothesis testing.
• Engineering for Data Collection: Understand engineering principles in creating instruments and applications for data collection.
• Effective Communication: Learn to communicate concepts effectively to diverse audiences.
• Accessibility and User Diversity: Recognise the importance of accessibility and diverse user needs.