Data Science in Manufacturing: How to Build Your Career
A new horizon of possibilities has become a frequent and unanimous theme of 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, consciously using natural resources to raise 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, careers on the rise, given the great competitiveness and search for innovation in different market sectors.
According to a study by Forbes magazine (2023), technologies such as big data, cloud computing, and artificial intelligence are the ones with the greatest adherence, by all business sectors, in emerging and developed countries. In numbers, about 75% of corporations plan to incorporate them in the next 5 years. Additionally, the jobs of data analysts and scientists, big data, AI, and machine learning specialists, and cybersecurity professionals are expected to grow by an average of 30% by 2027.
What is data science?
Data science is an interdisciplinary area that uses various types of 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 this way, in an industrial context, production line data – which until then has not been integrated – such as energy consumption, manufacturing time and quantity, or information from active machines, can be interconnected and classified in order to facilitate strategic decision-making for the business and generate savings on different fronts.
Understand how a solution applied in data science brings benefits to the industry here.
Professions within the field of data science
Within the area of data science, there are several subdivisions in which the professional can choose to specialize. The following will be presented the main ramifications, necessary both in the industrial environment and in other sectors that accompany the Information Age.
– Data Engineer:
The professional in this area is responsible for creating and implementing the system that extracts data from various sources. In addition, 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 also being responsible for ensuring good data protection practices and compliance with laws such as the 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 problem being investigated. To do this, he also needs 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 graphic and dynamic visualization of the 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 in any other sector, in the sense of contributing to the decisions made being aligned with the purpose of the business, and can, for example, direct it to be more productive and sustainable. Therefore, they are the ones who ask the right questions to the data, using the answers to understand patterns, for this they can pass demands to other professionals, in search of information that will be used as a basis for event predictions.
– Analista from Bussiness Intelligence:
Professionals in this area are focused on the business. Its main objective is to find and implement competitive advantages through the classification and monitoring of results inside and outside the industry. They develop reports, track market trends, and work with data scientists to suggest strategies and optimizations.
– Machine Learning Engineer
The machine learning engineer is responsible for building and implementing machine learning models. Their role includes taking the machine learning models developed by data scientists and ensuring that they are implemented effectively in production.
Interview with an Expert in the field of Data Science:
Immersed in his career
About his entry and career in the area of data science, Ismael Miranda tells us a little about his trajectory 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 Nanodegree in Data Science, and has been working in the area since 2014. Between one experience and another, he has worked with data focused on B2B, working within industries, and now practices the function by analyzing human behavior.
[ST-One] Ismael, could you introduce yourself briefly and tell us about the beginning of your career?
Ismael Miranda: I have a degree in chemical engineering, and at the university the focus has always been on designing chemical equipment. However, in my first internship, where I worked in a laboratory, doing sample tests, I soon came into contact with the data, setting up BI’s. While still in college, I had contact with the programming chair, which shone in my eyes, and when I realized that the job market also needed people focused on this area, I found myself.
When I was hired by a food industry, I started to specialize, programming several dashboards to be able to deliver reports, doing indicator analysis and Business Intelligence. Using the dashboards on a daily basis motivated me, and I started to find more and more places where it would be possible to monitor and extract valuable insights. I remember thinking ‘why don’t we look for 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 indispensable to develop in the area of data science?
Ismael Miranda: A professional who starts working with data has to have a technical skill, but also a business skill, having this skill refined opens up many opportunities, it is necessary 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 refining the professional more. He ends up more motivated, seeking more knowledge and specializing, being more prepared to try to understand and predict certain patterns.
The person must follow the concept of ‘microlearning’, seeking to keep up to date 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 give to those who are within the academic environment and want to enter the job market?
Ismael Miranda: My training helped me to deal with the technical part, since I started working in an environment familiar to chemical engineering. Technically understanding what was behind the data certainly helped me to understand it better, and to create more pertinent dashboards and reports, which would really show points of improvement for the laboratory. Even in my following jobs, I always had to study well the business in which the industry or company was inserted.
I believe that everything depends on commitment, curiosity, wanting to grow. It is always important to seek knowledge – in college what I would recommend is to take courses, there are several good ones being offered at all times, so the person is already 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, from hard to soft skills, have several qualifications, good communication, market knowledge. But when we talk mainly about technical skills, it is important to have a strong one, something that you find easy to exercise, 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 do with my eyes closed, without any problem.
Expert’s view on the future and upcoming trends
[ST-One] How do you see data science in the future?
Ismael Miranda: Currently, data in companies is more used for management, for example, my manager always checks the data we provide him, the reports need to be always up to date, 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 able to process, we will find more applications and 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 from analyses coming from the Business Intelligence sector. And for these areas to act in a cyclical way, it is inevitable to consider the business, and the people. It is necessary to know the purpose of the created graphic, of the programmed automation, so that the project runs consistently.
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 (PAIC Notebook, 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 the Dom Cabral Foundation (2023), in five years 44% of the technical skills required by the market will change. This even impacts careers in the technology area, as the so-called “hard skills”, which are highly valued in the market, will not be a single reason for prominence. To stand out, the ideal will 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 cover critical thinking and prior knowledge of the market in which this type of professional will work.
Another important factor, which is a differential 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 have dynamism, especially when suggesting new strategies and analyses that meet and exceed the client’s expectations.
In addition, 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 curiosity. Reinforcing the topic of soft skills, it is necessary to have empathy with the customer – always trying to understand the real issue to be solved – and sensitivity to business and market issues.
ST-One and the Career in Data Science
ST-One was created with the purpose of transforming 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 data-science-focused, industry-driven startup, we have a team of data scientists who nurture a close relationship 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 differential and meet new market demands, the professional who makes this happen is the data scientist.
Find out more on the ST-One careers page.
Tips on which courses to start your career in data science:
Cousera School: Professional Certificate Google Data Analytics
Codecademy School: Data Analysis with Python
Le Wagon School: Data Science Course