New age of Data & AI #1 : " The death of the Data Scientist, or birth of new Data Jobs in the Age of AI"

The increasing adoption of AI and ML has accelerated the debate about the future of the data scientist job. Some argue that AI will replace traditional data science roles, while others believe that new opportunities will emerge. In this article, we'll explore how the rise of AI is reshaping the data job market. The field of #Data and #AI has undergone significant evolution in the last 5 years, with a hype of maturity and the emergence of new key roles. The single role of "Data Scientist", once considered the only critical key-player in organizations, has evolved to include several other roles. This Darwinian evolution has resulted in the creation of more critical and specialized positions focused on specific aspects of the data pipeline.

• One of the new key roles that has emerged is the Data Designer. This role involves defining/designing the “unique norm of Data” with the literacy, models, topics, and ontology based on the core business of the organization. The Data Designer is responsible to create a unique vision of data in all of the organization and structuring the “data labels” and help all of the organization to have a “common language”. This role is critical to build a scalable Data process and go to the target of Data Driven. ?
• The role of Data Product Owner has also emerged as a key role. This role involves managing and defining the “data products” within an organization, ensuring that they align with business goals (OKR) and provide value to customers (all of the customers, including the corporate usage like Risk or Finance dept). The Data Product Owner is responsible to create a “data product”, as any another digital product for the customers. Chabot GPT is an example of "Data Product".?
• Data Analysts have been around for a while, named “Business Analyst” but their importance has increased significantly in the last few years. A Data Analyst is responsible for analyzing data, understanding the business needs and consume “Data Product”, to help the Data Designer for the common language and provide insights and recommendations to business to achieve their goals.
• The role of Data Engineer is another key role that has emerged in recent years. A big part of the Data Scientist is the preparation of the Data! Data Engineers are responsible for creating and managing the data pipeline, which involves collecting, processing, and storing data. The Data Engineer is responsible for ensuring that the data is of high quality and readily accessible to other members of the organization.
• AI Engineers and ML Engineers are both responsible for designing, building, and deploying software systems that incorporate AI and ML technologies. However, there are some differences between their roles.AI Engineers typically focus on the broader aspects of building intelligent systems. This involves selecting appropriate AI and ML algorithms to solve specific business problems, developing models and systems for NLP (natural language processing), computer vision, and other AI applications, and integrating these models with software applications. AI Engineers is also responsible for ensuring the scalability, reliability, and security of these systems. ML Engineers, on the other hand, focus more specifically on the development and optimization of machine learning models. They are responsible for selecting appropriate datasets and training algorithms, designing, and implementing ML pipelines, and fine-tuning models to achieve high levels of accuracy and performance. ML Engineers are also responsible for testing and validating models to ensure that they are performing as expected. Overall, both AI and ML Engineers work together to create intelligent systems that can analyze and make predictions based on data. Their roles overlap in many areas, and the specific responsibilities of each role may vary depending on the organization and the nature of the project.
• ML Ops is another role that has emerged as a result of the increasing use of machine learning in data analysis. ML Ops is responsible for the management and deployment of machine learning models in production.
• DataOps is another emerging role that is focused on the operations of data products on production. This role involves managing and maintaining the data infrastructure, monitoring and optimizing performance, and ensuring that the data products are always available and up-to-date.
• Data Owner is a person or team responsible for ensuring that the data within an organization is properly managed and utilized. They are accountable for the accuracy, completeness, and security of the data. The Data Owner sets data policies, establishes standards for data management, and identifies critical data elements. They ensure that the data is used in compliance with applicable regulations, and they work closely with the Data Steward to manage the data lifecycle.
• Data Steward is responsible for the day-to-day management of data within an organization. They ensure that the data is properly defined, classified, and maintained. They work closely with the Data Owner to ensure that the data is being used in compliance with applicable regulations and standards. The Data Steward is responsible for ensuring that the data is of high quality and is accessible to authorized users.
• Data Quality Manager is responsible for ensuring that data within an organization is accurate, complete, and consistent. They develop and implement data quality policies, procedures, and standards, identify and mitigate data quality issues, and collaborate with other data stakeholders. The role requires a deep understanding of data quality best practices and the ability to work with various data stakeholders. The Data Quality Manager plays a critical role in ensuring that data is of high quality, which is essential for effective decision-making, operational efficiency, and regulatory compliance.
• CDO or CDAO (Chief Data & Analytics Officer) is a senior executive responsible for the overall management of data within an organization. They are responsible for establishing the data strategy and ensuring that it aligns with the organization's overall business strategy. The CDO oversees the other data roles, including the Data Owner and Data Steward, and ensures that they work together effectively to manage the data lifecycle. The CDO also ensures that the data is used in compliance with applicable regulations and standards, and that it provides value to the organization. The CDO may also be responsible for identifying new opportunities for data use and data value.

?The past 5 years have brought significant changes to the data industry, leading to the emergence of new roles and the evolution of existing ones. This has resulted in a more specialized approach to data analysis, with a focus on improving project velocity and professionalizing data roles within organizations. The Data Scientist role is no longer the only key role in the data field, and has evolved into more specialized roles such as Data Engineer, Data Quality Analyst and ML Engineer.

As the field of data and AI continues to evolve, we can expect new roles to emerge. It is crucial to have a coherent strategy in place to arrive at game-changing solutions such as #GPT and the use of generative AIs as a product. This means staying up-to-date with the latest developments in the field, and being able to adapt to changes quickly.

Ethics also play a critical role in data and AI jobs. Professionals in these roles must be mindful of potential ethical issues and ensure that data is handled responsibly and in compliance with regulations. Companies must have strong ethical guidelines in place and ensure that their employees are trained to adhere to them. In conclusion, while the future of data and AI presents exciting opportunities for innovation and growth, it is crucial to approach these advancements with a strong strategic plan and a responsible, ethical mindset.

New age of Data & AI?#2 is coming soon :-)