The Uncommon Data Science Job Guide

Data scientists decrypt large data volumes and analyze further, aiming to discover trends in the data and acquire a more profound insight into its meaning.
You will run activities between the business and IT worlds. Besides, expectations are that you will run industries by analyzing sophisticated datasets to provoke insights that the organizations can leverage into actions.

Skills for a data scientist

Have you been wondering how to commence your career in data science? Hard skills such as analysis machine learning, analysis, statistics, Hadoop, amongst others. You also need to be excellent in critical thinking, listening skills, problem-solving and persuasive communications. Of course, you will now need training in data science.

The industry has plenty of opportunities. Therefore, once you get the education and qualifications, you will be assured of jobs now and in the future.

What common job titles should you expect?

• Data Mining Engineer

• Data Architect

• Business Intelligence Analyst

• Data Scientist

• Senior Data Scientist

How is the job market?

The data scientist role is amongst the most lucrative, with millions of global job opportunities in big data. Companies are making use of insights from data scientists to remain competitive while maintaining low overhead costs.

So, where can you find data scientist jobs near me? Brands such as State Farm, Boo, Allen Hamilton, Apple, Oracle, Microsoft, Walmart, etc., all have frequent job postings for data scientists. A data scientist jobs guide will give you the appropriate strategies to access them.

LinkedIn reveals that there are over 57,000 job opportunities worldwide. In 2019, data scientists emerged as the most promising profession.

Career Outlook

If you possess the right qualifications, you will enjoy the goodies that come with a career in data science. There is an increase in demand, and those already working are sure that a salary increases with time.

According to Glassdoor, a data scientist earns $108,224 per year. The salary ranges between $79,000 and $145,000 depending on the knowledge and experience.

With the career outlook and the job market overview, it is correct to say that securing a data science job is easy, right?

Wrong

Probably you may think that the process simply involves learning skills such as Jupyter and Python, participating in Kaggle competitions, getting a certification, and presenting your CV on job portals. However, the setting is entirely different. That's why you have to go through a data scientist job guide.

Just sometimes back when Kaggle landed 5 million unique users. Taking a look at other communities, Towards Data Science acquires 20 million views per month while there are 4 million students enrolled in the AI researcher Andrew Ng Coursera courses.

Currently, while there are 17,100 ML Engineers across the world, on the other hand, there are 2,100 open roles in LinkedIn and approximately 80 at the FAANGs.

Including the roles of AI researchers and general data science will give a figure of 86,000 open positions across the world. The question remains, how do I land data scientist jobs near me? Nevertheless, referring to data science as competitive would be an underestimation.

Red Ocean vs. Blue Ocean Strategy

In the business world, a 'red ocean' refers to a competitive ground where sharks are battling it out in a congested space. In contrast, a 'blue ocean' refers to an unoccupied market space free of competitors. As a potential data scientist, you aim to contest in the blue ocean, but how do you go about it?

1. 1. Make use of niche platforms and communities

   LinkedIn is proud to have more than 60% of a billion users. It is an ideal platform for networking and job applications. However, employers face a challenge due to the flooding of applications. It becomes difficult not only to send feedback but also to go through each of them.

   Therefore, as you use LinkedIn, you should also consider using niche platforms such as Y Combinator's, Lunchclub, and Shapr. Besides, you can use offline communities like Eventbrite or Slack communities such as Wizards. It is easier to stand out with tight-knight communities. Professional networks such as lunchclub and Shapr entirely focuses on one on one connections.

 

2. Avoid the "Black Hole Effect" by networking

   With applications flooding the hiring departments, submitting yours to some job portal is like throwing your CV in a black hole.

   Imagine yourself as a hiring manager, and there are two options, a stranger and someone introduced to you. Whom would you pick?

   Therefore, hiring executives will choose an applicant they have a connection with regardless of its depth. Create a larger network, and you will realize that there is more mutual connection with potential employers, and it will be easy for you to get an introduction.

   Below is a template you could consider to use to request a mutual connection for an introduction. The first step in creating a rapport will be to engage with their latest posts and send the message.

   Hello (Connector Name)

   I hope that you are fine. I was requesting you could introduce me to [@name] at [@company] as we are in the same profession and have a mutual connection. I recently applied for [job posting title] in the company.

 

3. Specialize

   Data science has multiple disciplines, with a significant component being domain expertise. Though the specialty will depend on your interests, learning specialized skills is a game-changer.

   Walmart foresees demand at specific hours through the use of predictive models. When hiring a manager to fill a data scientist position between two experts, one in "predictive modeling for retail," and another "Python," it is obvious they are likely to choose one who is more specialized.

 

4. Practical Projects > Certificates

   It's common to find Linkedin Users showing off their certifications from online-learning portals, EdX, Coursera, and other sources. It would be best if you had something to differentiate you from millions of people with similar certificates to yours. Practical projects whereby you carry out data analysis give you a higher edge.

Conclusion

Data science continues to become more competitive. However, the use of niche platforms will help you stand out. Besides, you should keep networking, specialize in a field that interests you, and share exceptional projects with the world. The above data scientist jobs guide has all that you need to land your dream opportunity.