Why are Data Science Skills So Important To Secure Your Automotive Job?

We are now living in a time of automotive innovation as technology is evolving faster than we could have ever imagined. Even though the pandemic has resulted in complications for many companies, especially in the automotive industry, new technologies such as DS, AI, and ML are still in high demand and continue to advance. The automotive sector has seen a significant transformation over the last decade. Nonetheless, you can still consider pursuing your career in this field and get a high-paying job.

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This blog will help you how you can secure your DS career in the Automotive sector. But first, let's dive briefly into How DS impacts this industry.

Data Science has always been at the core of transport product and production innovation. For instance, from being a mechanical machine that took us from A to B has now evolved into an intelligent machine that moves on its own! Interesting right?! Thanks to advanced technologies such as AI, ML, and Big data, that provide them with their own brain!

McKinsey estimates that a connected car generates about 25 GB of information every hour. Implementing this information can potentially change the auto industry's outlook. With that being said, DS, ML, and AI help in improving efficiencies at every level of the auto manufacturing processes, from research to developing new innovative products to better-serving customers and marketing processes.

What is the role of an automotive data scientist?

Data scientists ensure to make high-quality vehicles. They closely examine the entire process from testing parts, suppliers, and data. Their primary tasks include:

1. Analyzing the suppliers' financial performance

2. Estimating their ability to deliver on time on performance-based

3. Verifying the economic conditions of suppliers' locations using econometrics with regression.

For further information on the use of advanced data science and AI in the automotive industry, check out the Artificial intelligence course in Chennai.

How is data science used in the automotive industry?

With the rise in massive amounts of information, Data science impacts various stages in the automotive lifecycle. Let's dive deeper into how DS is applied in everyday automotive processes:

1. Manufacturing:

Manufacturing is the primary core step in developing auto machines. It is the production of goods with the help of equipment, machines, and tools. When it comes to manufacturing, AI-based techniques help automakers generate and manage schedules more accurately, enhance safety testing, and also detect issues in produced components. Additionally, predictive maintenance leads to a cost-effective and efficient manufacturing process.

1. Supply Chain Management:

Global supply chains are powering the auto sector. Supply Chain Management implies implementing software systems and management tactics to secure every step of the manufacturing process, starting from customer orders to ending with product delivery.

By using a machine learning-driven approach, it is possible to analyze large data sets to rank suppliers, credit ratings, and evaluations. This enables manufacturers to gain greater control over their supply chain, including logistics and management.

How to manage the supply chain in the automotive industry in simple steps:

• Using technologically capable software platforms to collect and organize information and analyze trends

• Maintaining the highest possible standards in all stages

• Ensuring all of your variables and fixed costs are justified to maintain and improve your margins

1. Research and Development :

In the coming days, AI will play a significant role in R&D productivity, preventing expensive R&D initiatives from being completely realized.

The sensor gathers enormous amounts of data from users, saving vast time and energy and allowing them to focus on projects with incredible promise. This extracted data can bring insight into the vehicle's usage, environmental consumption, and vehicular emission. Consequently, this will help in utilizing it for the regulatory and benefitting marketing trends.

1. Marketing and Finance:

In addition to benefiting the vast areas of the core business, Data Science and data analytics can also be used in other lines of business, including Marketing, Sales, and Finance, to introduce efficiencies that significantly impact automation in the bottom line. As in marketing, DS predicts customer movements and churns. It also improves the customer's post-purchase experience and improves the best quality. Some of the other use cases include Product Development, Sustainability, and city solutions.

How does big data benefit the industry?

As per the reports, the automotive industry has a vast amount of big data and is on the verge of rising. What actually is big data?

Big data analytics frames the premise of several applications as vast amounts of data are being collected through remote sensors. These are then probed and used to change the auto business, support mechanization, and boost automation. It is said that Data Analysis will most likely be the main impetus behind vehicle technology and progress in the near future.

The most crucial benefit is that the utilization of big data leads to a huge expense reduction for automakers by helping them investigate new strategies and using materials that give remarkable benefits.

What are the data science jobs you can do in the automobile industry?

From AI to big data, Independent self-driving cars to applications to sensors, this field is experiencing endless opportunities for unexpectedly crossing information. Apart from Data Scientist, you can apply for other job roles:

• Data Analyst

• Data Engineer

• Data Architect

• ML Engineer

• Business Intelligence Developer

• Business Intelligence Analyst

There are more than 14000 auto manufacturing companies in India where you can find multiple job opportunities. Some of them are Ford, Maruti Suzuki, Hyundai Motors, Tata Motors, and many others.

Where can you apply for data science jobs?

As the amount of data is expanding day-to-day, several automotive companies are in shortage of skilled data scientists. These are some of the companies hiring data scientists, along with their average pay scale:

1. Ford Motors (2-9 yrs experience): 13.9 LPA

2. Mahindra (2-7 yrs experience): 9.4 LPA

3. Mercedes Benz (2-7 yrs experience): 13.3LPA

4. Panasonic offers an average pay of 32 LPA

5. Maruti Suzuki (4-6 yrs experience): 13.8 LPA

Data science salary in the automotive industry :

Now let us look at the salary range of data scientists on different scales.

• Generally, the salary of a data scientist depends on experience, skills, and industry employment. As per the report, the automobile sector is considered the second top-paying industry for data scientists.

• The annual salary of a data scientist in the auto sector in India ranges from Rs. 5 Lakhs to Rs.32 Lakhs, with an average annual salary of INR 13 Lakhs, as mentioned by the Ambition box. However, It varies according to your experience level and skills.

Hot data science project ideas to optimize your portfolio

It is essential to showcase your skills and talents in your portfolio to qualify for the position you're applying for. Presenting your projects is the most powerful way to accomplish this. I'll list down some of the Automotive domain-based projects for you to gain proficiency in the field.

• The revenue forecast for Vehicle Financing

• Modeling and valuation of leasing contracts of Vehicles

• Interactive analysis for used car warranties

Final words

So far, now you're well-versed in how Data Science has been applied in nearly every sector to create a massive change. DS is required by businesses of all sizes to make decisions, analyze market trends, and boost revenues. If you're a DS aspirant and wish to make a career switch in the automotive industry, head to the IBM-accredited data science course in Chennai and become a pro data scientist.

I am Mallik and I am a passionate blogger who loves to write educational and technical content about data science courses, Machine Learning, and Artificial intelligence