How I Landed My First Data Scientist Job in MNC? – Top Secret Tips

Getting an entry-level data science job in today's competitive market. But if you have the right skill sets, anything is possible. The data science field is a constantly evolving field, and the number of applications is also increasing. Hence, it can be daunting to get a YES, or YOU ARE HIRED whenever you apply for jobs.

Like many other aspirants, I struggled to land my first data science job in a leading MNC. Yes, it wasn't as easy as it looked. It required a lot of effort and hard work. But no worries. Here, in this blog, I will share my secret tips on how you can succeed in securing a well-paying data science job. So buckle up and get ready towards a lucrative career now!

My Journey

Like many recent graduates, I struggled to find a job in my desired data science profession. I possessed a computer science degree, but I lacked the specific skills. I graduated from college with a computer science degree, but I quickly realized I lacked the necessary skills to work in data science. I was interested in data analysis, visualization, and machine learning, but I lacked actual expertise and skills in these areas. That's when I decided to improve my skills.

I began by researching the skills that were in high demand in this ever-changing sector. According to my research, data manipulation, programming languages, statistics, machine learning, and data visualization were among the top skills employers sought in a data scientist. With that in mind, I began my upskilling and upgrading my knowledge by enrolling in a data science program.

I chose an online data science course that provided a thorough curriculum and hands-on exposure to industry tools and applications. The course covered everything from the fundamentals of data manipulation to advanced machine learning methods. It also provided hands-on tasks that allowed me to apply what I learned in a real-world setting.

One of the important skills I learned was the ability to work with real datasets Additionally, I learned to collect, clean, and analyze data using Python and related modules such as Pandas and NumPy. I also learned how to visualize data using Matplotlib and Seaborn, which helped me acquire insights from the data and successfully convey it.

My Secret Tips

Tip #1 First off, Keep a Positive Mindset

Yes, Job hunting can be difficult, but don't give up. Maintain a positive attitude, continue to learn new skills, and stay focused on your goals. Only then will you succeed as a professional data scientist?

Tip #2 Be a problem-solver

The goal of data science is to solve real-world problems. Develop a problem-solving mindset and learn to think creatively and logically while approaching difficulties. So practice analyzing using real datasets from Kaggle and boost your problem-solving ability.

Tip #3 Work on mini data science projects using real datasets

Yes, Data Science projects can be challenging and involve huge databases. But they are really important for acquiring your job. It shows your practical skills and hands-on experience and not just your theoretical knowledge.

Be sure to stay organized, record your efforts, and monitor your progress.

Tip #4 Build A Strong and Impressive Portfolio

After finishing the training, I began working on my portfolio. I built a number of projects that demonstrated my expertise in data analysis, machine learning, and data visualization. These projects assisted me in demonstrating my abilities and attracting the attention of prospective employers. To obtain additional expertise and exposure, I also participated in online competitions and hackathons.

Employers want to see your capabilities, so build a solid portfolio highlighting your abilities and initiatives. Make sure it's simple to use, visually appealing and showcases your greatest work.

Tip #5 Start Networking

I learned how to network successfully as I continued to improve my skills. I participated in online communities, attended webinars and courses, and contacted data scientists on LinkedIn. These relationships assisted me in learning more about the sector and gaining insight into the job market. They also assisted me in obtaining referrals and introductions to prospective employers.

Connect with other data scientists by attending conferences and meetings and participating in online communities. This can help you discover job openings, acquire insight into the business, and get referrals.

Tip #6 Learn to communicate effectively

Data scientists must successfully communicate their findings and insights to stakeholders. Practice your communication skills and learn to convey data in a way non-technical people can understand.

Tip 7# Keep Learning and Never Stop

It's crucial to stay up to date with the most recent trends and technology because the data science profession is always changing. To stay current, take online classes, watch webinars, read blogs and trade periodicals, and join online forums.

Tip #8 Learn from Mistakes

Don't let yourself become bitter. Every rejection teaches you something. Ask for feedback after each interview and use it to guide your research. If there is no feedback, that is a lesson in and of itself.

Bonus Tip – Lastly, Never Give up!

On this journey, you will face a lot of hurdles, which you must get past. This is true even in the workplace for a data science professional. After being employed, it doesn't get any simpler. Think of it as practice for the real thing when it begins.

All my effort eventually paid off. I was hired by a startup with a lucrative pay. In order to increase user engagement, I was responsible for analyzing user data, producing visualizations, and developing prediction models. Although the job was challenging, my upskilling journey had well-prepared me for it.

Top In-Demand Skills That Helped me in my Career

Since data science is an extremely broad field, many people tend to get overwhelmed by the number of skills to learn to get an entry-level data scientist job. So, here are some of the skills that helped me get the data science jobs.

  1. Programming is the Key

If you don't like programming, you are lagging behind. It is the foundation of data science.

Be adept at Python, R and SQL languages.

Python Programming: You might know that Python is the most popular programming language in data science; thus, proficiency in Python is a must-have skill for a data scientist. Writing clean, efficient code and utilizing Python libraries, including Pandas, NumPy, and Scikit-learn, was essential for me to succeed in my job.

  1. Statistics

Previously, data science was all about statistics and were used interchangeably. Hence, a strong statistical foundation is a must for becoming a data scientist. Understanding concepts such as hypothesis testing, probability, and regression analysis helped me in making informed decisions and gaining valuable insights from data.

  1. Data Visualization

The ability to visualize data and present it in a way that's easy to understand is an important skill for a data scientist. Using tools like Matplotlib, Seaborn, and Tableau, I was able to create clear, concise visualizations that helped me communicate my findings effectively. Hence, mastering data visualization tools can make your workflow easier.

  1. Data Manipulation

Data scientists invest significant time in cleaning, preprocessing, and modifying data to prepare it for analysis. I needed to understand how to work with various sorts of data and technologies, such as SQL and Pandas, to analyze and interpret data successfully.

  1. Machine learning

Machine learning is an important skill for a data scientist, and employers seek people who understand machine learning algorithms and can apply them to real-world issues. It was critical for me to use Scikit-learn to develop techniques like linear regression, decision trees, and random forests.

  1. Big Data technologies

With the amount of data collected every day increasing, data scientists must have a fundamental understanding of big data technologies such as Hadoop, Spark, and MapReduce. Knowledge of these tools can help data scientists handle enormous datasets more efficiently.

  1. Soft Skills

Data scientists do not work alone, and soft skills like communication, teamwork, and problem-solving are vital for success on the job. It was critical for my success in my work to be able to communicate effectively, interact with others, and tackle problems creatively.

These were some of the most in-demand skills that assisted me in securing my first data science position. However, It is crucial to note that the specific abilities necessary may differ based on the industry, organization, and work function. Staying current on industry developments and innovations is critical to competitiveness and finding your dream job.

What is the Starting Salary of a data scientist?

As per my research, a data scientist in India makes an annual salary of Rs. 8,60,316.

According to 500 data scientists' salaries in India, freshers and budding data scientists with less than a year of experience get an average salary of Rs. 5,71,493. On the other hand, data scientists with 1 to 4 years of experience make an average of Rs. 8,00,750 annually.

So you will expect a decent salary when you start your data science career.

Begin Upskilling Yourself now!

Throughout my career journey, I learned that upskilling is important in landing your first data science job. You can stay competitive and fulfill the field's ever-changing expectations by constantly learning and acquiring new abilities. I've highlighted the top skills that were crucial in my achievement based on my own path and experience.

I hope my insights have provided you with useful tips and motivation to help you reach your own data science career objectives. Remember that the most important thing is to keep learning and staying positive. You can definitely acquire your desired data science job and flourish in this exciting industry if you follow these suggestions and are dedicated to your trade.