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How To Make a Rewarding Career in the Energy, Oil, and Gas Domain as a Data Scientist?

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The oil and gas sectors have been the most lucrative arena for most chemical engineers, petroleum engineers, mechanical engineers, and even geologists (petroleum). Not only the private occupations but also the government job scopes are quite high in this subject. But beyond all kinds of expectations, people in these industries are at significant risk of losing their employment. So what occurred all of a sudden!!

Well, no need to get panic. We all know that if one road gets blocked, certainly there are other ways to escape. Data Science and AI are the ultimate escape route from this roadblock. This blog talks about data science in energy, oil, and gas industry. But first, let’s have a look into the ‘what’s happenings?’’

According to PetroLMI, which provides industry labor market information, the unemployment rate, especially for oil and gas workers, peaked at 16.1% in 2020, 26% below 2014 levels. People working in this industry are quitting by themselves due to the lack of growth. _People who have more experience are also not in the green zone. What is important to keep your ground in the domain is the skills that you have. You need to keep upskilling only then you will be safe. Even the oil and gas industry lives and breathes on data and has been going digital as well. It is very possible that not only will you retain your position in the industry but also have good pay as a Data science specialist.

Data Science salary is a hot topic everyone thinks about when becoming a data scientist. We all have heard praises about how lucrative the DS field is. So much so that data scientists are notorious for being pompous. But I wouldn’t blame them. A data scientist makes big bucks in any and every domain. It is a very versatile field, and the domain-specific approach of specific organizations makes it a game-changer for many people looking to make that career switch.

Now that we have established that DS is a very profitable field and there is a need for it in any field, I would like to tell you something very particular. That is the importance of data science in energy, oil, and gas industry. So let us start by asking some critical questions.

Use of Data Science in Energy, Oil, and Gas Industry

Image by Author

The oil and gas industry misses boats in data science, a reinterpretation of the inherent value of data and its strategic assets. Like other industries, today’s oil and gas industry is looking for ways to improve efficiency, thereby reducing operating costs and increasing revenue.

However, oil and gas companies are also subject to exceptional safety, environmental, and regulatory reporting requirements in contrast to many industries. Therefore, data science has many advantages that can help improve data efficiency and increase sales when adopted in the industry. With this, I think you can make out that DS is essential in the domain we are talking about.

Need for Data Science in the Energy domain

  • The amount of data in the oil and gas industry increases exponentially due to advances in information technology.
  • This includes everything from sensor recording during exploration, drilling, production, and seismic manipulation to in-drill logging (LWD) technology that enables real-time recording of drilling data.
  • Also includes fiber optic solutions that provide a wide range of data on environmental conditions such as temperature, oil reserves, equipment performance, and condition. Managing this data and using it as a strategic asset can significantly impact its financial performance.
  • The plunge in oil prices has forced oil and gas companies to go beyond traditional methods to seek broader changes in business practices to improve performance and reduce costs.
  • Better data analysis and technology are essential in determining the success of oil and gas companies.

Advantages of Data Science in the Energy, Oil and Gas Industry.

As of now, we have established that data science has found its uses in various fields. That is because the DS has multiple advantages no matter where you want to use it. Such is the malleability of this field. It has numerous advantages in any field that it steps foot in.

Here are some high-level examples of how data science can help the oil and gas industry.

  • Exploration and Discovery-Geological data such as seismic data and rock types in nearby drilling holes can predict oil pockets.
  • Production Accounting You can link production data to alarms.
  • Drilling and Completion-Predictive Analytics can use geological completion and drilling data to determine preferred and optimal drilling sites.
  • Equipment Maintenance-Compare real-time streaming data from oil rigs with past drills to better predict and avoid problems and understand operational risk. These examples show the operational goals of oil and gas data science.

As with other technological advances, there are barriers to the successful use of data science: (Disadvantages)

  • _Taxing Compute Resources You may not have enough resources to store and process large amounts of structured and unstructured data.
  • Poor data quality-Data is stored in multiple locations and can be subject to inconsistent governance.
  • Incorrect Modeling– The correct question may not have been asked or misunderstood.
  • Relentless Corporate – C-suite support is essential from the start. Communication between employees, SMBs, and data scientists is essential.
  • Talent Gap: Data science and data engineering talent is new to the oil and gas industry. These skills are still under development, and it can be challenging to put together the right team.

Projects To Level Up Your Resume

When we talk about entering into a specific specialization job market, it is essential to have the proper skills displayed on your resume that make you fit for the role. The best way to achieve that is through projects. These are some of the Oil and Gas domain-based projects that you can do to become a DS specialist in the field.

  • Prediction of cost overruns in Oil and Gas Engineering_
  • Developing a Failure Prediction Model_
  • Model for determining the optimum and efficient use of machines._

What are companies hiring for Energy, oil, and gas data scientists?

Data science, a versatile field, is also very lucrative for various fields and companies. These are some of the oil and gas companies that are looking for data scientists.

Image by Author

Source: Linkedin

  • Schlumberger, Cambridge will offer INR 1,468,040 per annum
  • Saudi Aramco offers INR 1,986,586 per annum
  • Praxair pays INR 997,500 per annum
  • BP will offer you INR 1,350,000 per annum
  • The total gives INR 1,080,000 per annum
  • And finally, National Iranian Oil Co (NIOC) offers INR 1,750,000 per annum

Now that the whole domain is sorted. Let us come to the salaries that you have been waiting for. Brace yourself for the mind-blowing information you will be bombarded with.

How much do data scientists earn with different variables?

Since we will primarily be discussing the pay range and living of a data scientist, let us see exactly how much data scientists in different roles and fields earn.

On average, it is estimated that a fresher in data science earns about Rs.6,98,412 as base pay in a year. This is subject to variation with every organization. However, the figures will be more or less near to the given value. Let’s see how much DS can get you according to your experience in the field.

Based on Experience

  • Freshers: The average income of entry-level data scientists is Rs 511,468 per year for recent graduates.
  • 1-4 years of experience: With 1-4 years of experience, his early career data scientist earns an average of 773,442 rupees per year.
  • 5-9 years of experience: Employees with 5-9 years of experience can expect an annual income of 12-14 rupees. The average salary-scale income of mid-sized data scientists is Rs 1,367,306 per year.
  • Over 10-15 years of experience: Very experienced employees with decades of experience or managerial positions can expect to earn anywhere from 24 lakh rupees a year to a healthy crore.

Based on Location (India)

The essential factor that can affect your salary as a scientist can vary from place to place based on the demand in the region. So let’s see how much you’ll get paid in a particular place.

Image by Author

Source- Glassdoor

  • In Mumbai, you’ll get paid Rs.788,789 per annum
  • Chennai will pay you Rs.794,403 per annum
  • In Bangalore, you will make Rs.984,488 per annum
  • In Hyderabad, you can get Rs.795,023 per annum
  • In Pune, you will get a salary of Rs.725,146 per annum
  • In Kolkata, you will get paid Rs. 402,978 per annum

Based on your skills

Believe it or not, the salary you get depends heavily on the field’s skills you have learned. So let’s see how much you can make.

  • Knowing R is the most critical and demanding skill, followed by Python. Python’s salary in India is projected to be around 10.2 lakh rupees per year.
  • If data analysts have both big data and data science, their income will increase by 26% compared to just one piece of knowledge.
  • SAS users are paid 9.1-10.8 lux per year, and SPSS Professionals are paid 7.3 lakhs per year.
  • Machine Learning salaries in India start at around Rs 3-5 lakhs and can rise to Rs 16 lakhs as the industry progresses. Python is one of the most popular Machine Learning languages, and Python developers in India have the highest salaries globally.
  • Knowledge of artificial intelligence generally helps advance your career. Artificial intelligence payments in India are over 5-6 lakhs rupees for beginners in this field.

So these are all the variables that can affect your salary and how much you will get paid. To me, it is impressive. So I think you should start thinking about that career switch carefully.

Data Science Salaries in Other Countries

If you decide to go to a different country with the skills you have learned, you might be interested in learning about how much you can make. Don’t worry; I got you covered.

  • United States

The United States is at the top of the list of countries that pay high salaries to data scientists willing to work for it. The average annual salary for US data scientists is $ 120,000 per year. Data scientist rewards are higher than in any other country.

  • Australia

Australia is ranked second in the list of countries that make high payments to data scientists. This is evidenced by the influx of data scientists from Australia and other countries into the United States. Average salaries for data scientists range from $ 75,233 to $ 121,578 per year, based on experience.

  • Germany

In Germany, job seekers in the data science sector earn up to € 5,960 a month. Working data scientists in Germany earn € 2,740-9,470 a month.

If you have stuck with this blog till now, you are either a budding DS aspirant or someone interested in the field or to make a career switch. So you might be thinking about how you can develop your skills. So let me tell you some resources to help you get those skills to land you that beautiful package.

How would Learnbay help you in this domain?

I highly recommend Learnbay because it provides one of the best data science training in Bangalore. It is also available online and provides some of the best features even in online sessions. Let me tell you about some of the features, and then you will know why I love this institute so much. According to an article by analytics insight, Learnbay is one of the most acknowledged data science institutes.

I prefer Learnbay so much because of the choices of electives it provides. Out of the leading electives, Oil and Gas is also one of them.

What will you learn if you choose this elective?

  • You will get to learn about tools like GitHub, Python, Power BI, Tableau, and more such tools regarding the particular domain.
  • You will learn about concepts like:
    • Analysis of seismic data and microseismic data,
    • Decreasing drilling time and boosting safety
    • Improving occupational safety
    • Production pump performance and so on
  • You will also get to work on energy-based sectors like detection and prediction of a power outage, Power Failure Prediction, Dynamic Energy Management, and many more.
  • This domain comes with 2 modules:
    • In the first module, you will learn about the role of analytics and data science in the field of Energy.
    • In the second module, you will see the role of analytics and DS in the Oil and Gas field.
  • There are about 2 Capstone projects in this domain specialization.

Projects in the Energy and Gas domain

With Learnbay, you get to see the theoretical applications of the concepts and apply them in practice by working on various projects that will increase your expertise in DS concerning the domain you have chosen. So let’s now see the projects that you will get to work on if you choose this specialization by Learnbay.

  • Developing Smart Grid Security and Theft Detection Model (Energy Domain)

In this project, you will learn to build intelligent grids that usually go directly to the distribution cable. This will detect any kind of malicious activity in the energy resources and therefore lower the rate of resource-based thefts and fraudulent actions.

  • Developing Model Developing a Model for High Accuracy in Drilling Methods and Oil & Gas Exploration.

Drilling is a significant part of the process of extraction. Many of us will also be aware of the risks that it may pose to both the people working and those around them. Therefore, it is essential to develop a safe and effective method that will increase both the safety and accuracy of the whole process, which is why this project will allow you to develop such a model.

Kaggle dataset for oil industry price fluctuations

These are some of the DS courses on the platform.

  • Data Science and AI Certification | Domain Specialization for Professionals: This course is intended for professionals with at least one year of professional experience. The course lasts 7.5 months.
  • Data Science and AI Certification Program for Managers and Executives This is a unique course for professionals with over 8 years of experience as managers, team leaders, or in other prominent positions. The project period is 11 months.
  • Data Science and Business Analysis Program | Fast Track Course This 4-month course is intended for those who have taken a professional break of 6 months or more.

To conclude it is very evident by now that data science in energy, oil, and gas industry has been booming. You can make a good career in the discussed domain while being a data scientist if you follow the tips and tricks that have been mentioned above. I hope you continue learning and growing in the data science field and prove to be an asset to the community.

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