With a decade of experience, Hariom is a visionary entrepreneur & creator, unveiling opportunities, new media/upcoming technology solutions, and digital products in the dynamics of technological landscapes. He is a savvy entrepreneur, curating to craft a future by making meetings and events immersive.

We live in a data driven world and data is king, making it a vast untapped resource that has the potential to immensely change the world. Fields like Data Science and Analytics tap into this uncharted resource and transform lives for the good. Businesses have realized that having access to the right information at the right time can boost productivity and streamline processes. Therefore, most companies are applying data driven decision-making and are utilizing predictive analytics to boost the organization's productivity, streamline operations, and boost revenue.

High demand for understanding and using data has increased the demand for skilled professionals in AI, more specifically data science and data analytics. While most jobs offered in data science/analytics are from the IT services/consultancy and software development industry. Industries such as healthcare and investment management too require data analysts/scientists. AI is a vast field encompassing various genres. These fields require technical and nontechnical job roles which are as follows:

Career Progression in Technical Job Roles

1.Data Analyst: Data analysts interpret and analyze collected data, identifying trends crucial for strategic business decisions. They utilize SQL, R, SAS, and visualization tools like Power BI and Tableau. Strong communication skills are essential as they convey findings to non-technical teams. Typically beginning with internships, they progress to entry-level positions, then to senior roles involving team management and project planning.

2.Data Engineer: They are like software engineers specialized in data, gathering data from diverse sources to construct a data warehouse accessible to the entire organization. They gather, manage, and transform raw data into usable formats for data scientists and analysts. These systems guarantee seamless data flow from multiple sources to the data warehouse or Data Lake, ensuring uninterrupted access for end-users without loss or corruption.

3.AI Engineer: In this role the individual utilizes data to develop models capable of predicting or making decisions autonomously, without explicit programming for the task. The responsibilities of an AI engineer include: understanding the business challenge, crafting a solution, coding the model, and deploying it.

4.NLP Engineer: They are AI engineers choosing to specialize in natural language processing. They design computer systems that enable computers to understand, interpret, and generate language. They primarily work on chatbots and voice assistants. They work on a combined knowledge of computer science, Artificial Intelligence, and linguistics that help humans and machines to communicate.

5.CV Engineer: These are AI engineers choosing to specialize in computer vision. These engineers help machines understand the visual world around them by interpreting algorithms that understand digital images. They train computers to do tasks including object detection, image classification, and facial recognition. They work on specific tasks like object detection, image segmentation, and 3D reconstruction.

6.Data Scientist: Data scientists focus on creating algorithms and predictive models for data analysts, aiding organizations by developing tailored methods and tools for data extraction and task automation. Interns start by analyzing and preparing data, learning software like SQL, Excel, Python, and R. Those excelling may receive pre-placement offers and become junior data scientists, and working closely with seniors & engineers. Progression from junior to senior data scientist involves managing teams and long-term project planning.

From analyzing the data to interpreting the evolution of every industry and business, Artificial Intelligence is the driving force for innovation. So, re-skilling and up-skilling are paramount for scaling up knowledge and skills

Non- technical Job Roles:

1.AI Product Manager: This role is right at the intersection of business and technology. AI PMs work with stakeholders to understand their demands from a product, the product goals, product features and the market. They also work with the developers to build AI products that fulfill customer needs. They act as a bridge between the business and the technical team. They don't necessarily code but have a very good understanding of various AI concepts.

2.AI Sales Executive: A core member of an AI-focused sales team is responsible for selling AI products and services to the right customers. They require a deep understanding of the AI tools and services they offer. They are the trusted partner of leading innovators and work on growing their relationships and winning new work for the growth of the organization. Their main goal is to understand the needs of potential customers and accordingly offer AI solutions to the customers.

3.AI Ethicist: They bring to light problems that AI innovations can bring to the society as a whole. They ensure that as AI is developed and deployed it is done responsibly. They deal with AI and its implications on humans such as:
•Preserving human rights
•Building trust in AI systems
•Promoting responsible AI research and innovation
•Understanding and addressing the economic impact of AI
•Dealing with damage done by AI

Opportunities in data science and analytics are growing rapidly. For businesses and society to truly leverage it, we must transform the education system and encourage collaborative learning. Experiential learning must be prioritized over rote learning. Hands-on experience in the fields mentioned previously, can be achieved through collaborative interdisciplinary projects.

Therefore, it is paramount to fully bridge the gap between skill and opportunity. This can only be done if educational institutes and industry leaders learn to collaborate. Data science and analytics have the potential to solve real world problems. The challenge is that not many people understand data but those who do are the rulers of tomorrow.