Career in data science

Data Science in Industry: How to Build Your Career

A new horizon of possibilities has become a frequent and unanimous topic in discussions about the future of the industry: how to use the billions of data generated every day, by machines and sensors, to extract even more value and performance from the factory floor. This discussion involves several issues, such as increasing performance, conscious use of natural resources to increase sustainability levels, improving the decision-making process, and allowing intelligent investment in research and development. The key professional in this context is relatively new but essential: the data scientist.

For this culture to materialize, including in the industrial environment, different fronts of professionals focused on data emerge, and careers are on the rise, given the great competitiveness and search for innovation in different sectors of the market.

According to a study by Forbes magazine (2023), technologies such as big data, cloud computing, and artificial intelligence are those with the greatest adoption, across all business sectors, in emerging and developed countries. In numbers, around 75% of corporations plan to incorporate them in the next 5 years. Additionally, jobs for data analysts and scientists, big data, AI and machine learning specialists, and cybersecurity professionals are expected to grow by an average of 30% through 2027.

What is data science?

Data science is an interdisciplinary area that uses different tools to identify patterns and gain insights from raw data collected from different sources, whether from machines or people. This process includes the capture, processing, transformation, and analysis of this information.

In an industrial context, data from the production line – which until now has not been integrated – such as energy consumption, manufacturing time and quantity, or information from active machines, can be interconnected and classified to facilitate strategic decision-making for the business and generate savings in different areas.

Understand how an applied data science solution brings benefits to the industry here.

Professions within the area of data science

Within the area of data science, there are several subdivisions in which professionals can choose to specialize. I’m sharing the main ramifications, necessary in the industrial and other sectors, below.

– Data Engineer:

The professional in this area is responsible for creating and implementing the system that extracts data from various sources. Furthermore, it performs the necessary processing and makes the information available in an orderly and useful way for it to be consumed by analysts and data scientists.

Therefore, the main focus of this segment is on how to structure the database, in addition to being responsible for ensuring good data protection practices and compliance with laws such as LGPD by the industry.

– Data analyst:

The data analyst is the one who performs the diagnosis and has significant insights from the information that is collected. The professional analyzes and interprets the data, and understands the behavior patterns of the investigated problem. To do this, they also need to understand the business context in which the failure is inserted, in addition to understanding the reasons behind the procedures found. Finally, he is usually responsible for creating dashboards, where the graphical and dynamic visualization of data is made.

– Data Scientist:

These professionals are characterized by a strategic, mathematical, and analytical vision. This is because it is their role to study behavior and understand the predictive probabilities of the consequence of the data in question. This is reflected in the industrial environment, or any other sector, in the sense of contributing to ensuring that the decisions made are aligned with the purpose of the business, and can, for example, guide it towards being more productive and sustainable. Therefore, they are the ones who ask the correct questions to the data, using the answers to understand patterns, so they can pass demands on to other professionals, in search of information that will be used as a basis for event predictions.

– Business Intelligence Analyst:

Professionals in this area focus on the business. Its main objective is to find and implement competitive advantages through classification and monitoring of results within and outside the industry. They develop reports, monitor market trends, and work together with data scientists to suggest strategies and optimizations.

– Machine Learning Engineer

The machine learning engineer is responsible for creating and implementing machine learning models. Your role includes taking machine learning models developed by data scientists and ensuring that they are implemented effectively in production.

Interview with Expert in the field of Data Science:

Immersed in career

Regarding his entry and career in the area of data science, Ismael Miranda tells us a little about his career and gives tips for those who want to work in this field. Ismael has a degree in Chemical Engineering from UFC (Federal University of Ceará), a Nanograduate degree in Data Science, and has been working in the area since 2014. Between one experience and another, he has worked with data aimed at B2B, including working within industries, and now practices the function by analyzing human behavior.

[ST-One] Ismael, could you briefly introduce yourself and tell us about the beginning of your career?

Ismael Miranda: I have a degree in chemical engineering, and at university, the focus was always on designing chemical equipment. However, in my first internship, where I worked in a laboratory, carrying out sample tests, I soon came into contact with the data, creating BIs. While still in college, I had contact with the programming chair, which caught my eye, and when I realized that the job market also needed people focused on this area, I found myself.

When I was hired by the food industry, I started to specialize, programming several dashboards to deliver reports, analyzing indicators, and Business Intelligence. Using the dashboards daily motivated me, and I started finding more and more places where I could monitor and gain valuable insights. I remember thinking ‘Why don’t we look at how to collect more data? Develop other types of dashboards, to monitor more information and meet the needs of the industry.

[ST-One] What do you consider essential to develop in the area of data science?

Ismael Miranda: A professional who starts working with data must have a technical skill, but also a business skill, having this skill refined opens up many opportunities, and you need to have market knowledge. I saw this happen to me when I started doing predictive analysis, because this curiosity, this search for new knowledge, ends up improving the professional. He ends up more motivated, seeking more knowledge and specializing, and being more prepared to try to understand and predict certain patterns.

The person must follow the concept of ‘microlearning’, seeking to stay updated daily, and, as it is a dynamic area, there is a feeling of always evolving, with the constant need to continue learning.

[ST-One] Do you believe that your trajectory and training contributed to you being able to start in this market? What advice do you have for those in the academic environment who want to enter the job market?

Ismael Miranda: My training helped me to deal with the technical side, as I started working in an environment familiar with chemical engineering. Technically understanding what was behind the data certainly helped me understand it better, and create more relevant dashboards and reports, which would show areas of improvement for the laboratory. Even in my subsequent jobs, I always had to study the business in which the industry or company was located.

I believe that everything depends on commitment, curiosity, and desire to grow. It’s always important to seek knowledge – at college what I would recommend is taking courses, several good ones are being offered at all times, this way the person is a little more ready for the market, where they will feel the real application.

[ST-One] How did you direct your studies?

Ismael Miranda: The professional has to know a little bit of everything, ranging from hard to soft skills, have several qualifications, good communication, and market knowledge. But, when we talk mainly about technical skills, it is important to have strong ones, something that you find easy to use, regardless of the context. It is important to specialize and master at least one tool. For example, what took me very far was a specific tool, Power BI, which I use with my eyes closed, without any problems.

Expert insight into the future and upcoming trends

[ST-One] How do you see data science in the future?

Ismael Miranda: Currently, data in companies is used more for management, for example, my manager always checks the data we provide to him, the reports always need to be updated, and everything else.

I believe that the evolution will be in the application of data, regardless of the market in which it operates. The more information we have, the more computers are capable of processing, the more applications we will find and we will be able to create more models, for example, machine learning.

In industries, I see the combination of data science and automation. There are already industries, for example in the oil sector, that use models programmed based on analyses coming from the Business Intelligence sector. And for these areas to act cyclically, it is inevitable to consider the business and the people. It is necessary to know the purpose of the created graph, of the programmed automation, so that the project runs consistently.

Career in data science

How to become a data scientist

The data scientist must have an analytical, strategic and mathematical profile, to be able to analyze the needs of companies in a logical and practical way (Caderno PAIC, 2023). As seen, regardless of the field of activity in this area, heterogeneous knowledge and skills are necessary.

According to the study “Future of Work 2023”, carried out by the World Economic Forum with support from Fundação Dom Cabral (2023), in five years 44% of the technical skills required by the market will undergo changes. This even impacts careers in the technology area, as the so-called “hard skills”, which are highly valued in the market, will not alone be highlighted. To stand out, the ideal would be to balance them with the so-called “soft skills”, which in this case, in addition to including skills such as resilience, emotional intelligence, effective communication and good time management, also encompass critical thinking and prior knowledge of the market. in which this type of professional will work.

Another important factor, which is a differentiator sought in this area, is proactivity. As it is a profession that uses many tools and programming languages, the technical profile is very common, but it is necessary to be dynamic, especially when suggesting new strategies and analyzes that meet and exceed the client’s expectations.

Furthermore, as it is an area that has a range of possibilities, the professional must always be up to date and seeking to learn, with a tendency to be curious. Reinforcing the topic of soft skills, it is necessary to have empathy with the customer – always trying to understand the real issue to be resolved – and sensitivity to business and market issues.

Career in data science

ST-One and a Career in Data Science

ST-One was created to transform the industry to a new level of productivity. The science developed by ST-One is improved with each new challenge and makes it possible for digitalization, present in different industry sectors, to reach the next stage of connectivity and intelligence.

As a startup focused on data science and focused on industry, we have a team of data scientists who nurture close relationships with customers. Always aiming to arrive at the best analysis, we act directly on the customer’s pain and deliver more optimization and assertiveness. It is through constant study, improvement, and direct contact with partners and businesses that our solution is developed in a dynamic and personalized way.

Technology and data science are not just tools, but strategic partners for industries to obtain a difference and meet new market demands. The professional who makes this happen is the data scientist.

Find out more about our team by exploring our careers page here.