How Hard Is It To Get A Data Science Job

Why are Information Scientific discipline Jobs Hard to Become When They're the Most In-Demand?

I think it's time we address the elephant in the room

In the rapidly expanding technological earth of today, people from the right, left and center are starting their career in data or making career shifts after decades of experience in finance or going back to school to get a Data Science degree. Having navigated the information science job market for securing internships and full-time offers recently, I've had a starting time-manus see with the realities of what I'd like to telephone call — a coconut field.

Job hunting is tiring; naming 496th draft of your resume as the final version to the lineup of interviews, companies ghosting you lot after four rounds of interview and countless LinkedIn messages to connect and for referrals — if you were lucky plenty to hear back with an offer alphabetic character, your peace is granted only when you experience confident enough to post that LinkedIn update on your 24-hour interval 1.

An increasing number of people are calling themselves data scientific discipline enthusiasts today. While the main reason for the exponential growth of information science candidates is believed to be the growth in the number of information chore openings, getting a Information Science task is harder than ever.

But, there is no losing promise here if you correctly identify the expectations of the task market. The point is to identify what works the best for you lot and use it to your advantage in matching with the best arrangement for your goals and interests.

5 or six years ago when data was non at the core of business conclusion-making, companies would manufacture a information professional per the needs of the organizations, and with an increasing need to analyze data, these companies were helped past academia and kicking-camps with preliminary skills.

Statistics are a affair of the past

When Harvard Business organization Review said "Data Scientist: The Sexiest Chore of the 21st Century", recollect — that was 2012 (apparently, the earth was supposed to come up to an end that year 😀)

The statistics yous read online do not represent the dynamics of today.

The needs, demands, and expectations of an system have changed drastically. Even the about updated statistics around Data Science jobs are at least a quarter old and data science is always-evolving. From 2016, Information Scientist was the number i job in America but in 2020, it came third (Glassdoor's annual ranking) and who knows by 2025, information technology might not even exist in the top ten.

Information technology of course depends on y'all how yous perceive those statistics just if a website says 'There xx million data science jobs in 2021', does not mean they are all open for yous.

Rather than reading numerous manufactures online on-need of the Information Science market, I recommend connecting to a Data Scientist in your network (or make new connections) and acquire more most the mural from those conversations. Read virtually the new projects your companies of interest are recently working on or connect to your peer from xv years who just changed their task from Data Scientific discipline to Business Intelligence Analyst for their honest opinion.

There's much more to the information science unicorn

Data Scientific discipline job requirements are changing every twenty-four hour period for every organization, for every line of business. The field is becoming more productionized, more concern-oriented. Data Scientists are expected to brand upwardly for a Data Engineer and a BI Analyst on the same twenty-four hours.

I tin can name fifty job profiles that do non involve the name information only you piece of work on ane of the processes of the Data Science life cycle. If there's 1 thing I learned from the job hunt of my classmates (class of 2021) — you cannot limit yourself to filtering job search to keywords Data Scientist and Data Analyst.

Below are a few exciting job titles similar to that of a Data Scientist you lot tin build your skills for and employ to —

It's not always nigh the candidate

Candidates often fail to communicate their worth in interviews.

Often, candidates with a three.four GPA beat out a candidate with a perfect four.0 GPA in an interview. During interviews, theoretical cognition might contribute to only xl% of your overall cess. But, having worked on applied projects, collaborated in teams or enquiry, internships in schoolhouse — the ability to communicate those, articulate what unique strengths and qualities you bring to the table is what can make the game for you.

While companies are looking for candidates with considerable experience in the tools and skills listed on the job description to add together value to the team, a resume cannot e'er convey the intended. In such cases, information technology'southward non the candidate, it's their resume-making skills that need a little focus. What moves the needle for a candidate is the chat mail service resume screening.

Businesses seek professionals who can excel at forming theories, testing hunches, finding patterns that allow the business organisation to predict profit-making scenarios or troubleshoot the lows; a candidate who has the potential to propose avenues for capitalizing on the heuristics developed and thereby add value to the users. A new grad is sure to get nervous to answer fiscal analytic questions in a six-hr console interview. If you're someone hiring candidates, a conversation to make your candidate comfortable would be highly appreciated.

Companies want unicorns for their data teams; how about we discover an analyst who tin predict how to reduce the marketing costs and increase customer acquisition?!

The herd mentality

Just considering people say jobs in Data Science are hot, information technology seems like everyone wants to jump on the bandwagon with merely a few days of training. From what I've learned in my 3 years of Data Scientific discipline journey— it is not a field where a 15-twenty-four hour period course can lend yous a job. Data Science requires a discipline, a gear up procedure while learning.

The applicants in the current task marketplace accept merely enough knowledge to exist unsafe — cognition nigh what they know and what they desire to practise.

In the real world, you exercise not see a job posting for MBA jobs or computer science jobs. They are degrees, not jobs. The reason businesses are seemingly more interested in hiring people into data science job titles is considering they recognize the emerging trends and need of cloud computing, big data, AI, machine learning.

It'south time to widen the job search and not limit to Data Scientific discipline.

That's information technology from my stop for this weblog. Thank yous for reading! Allow me know in the comments if you lot too faced struggles while searching for a data science job or are searching for one. I would beloved to know more than about your journey and thoughts.

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Happy Data Tenting!

Rashi is a information wiz from Chicago who loves to visualize data and create insightful stories to communicate business insights. She's a total-fourth dimension healthcare data analyst and on weekends blogs about data with a good cup of hot chocolate…