How to Choose a Data Science Job
All Data Scientists worth their salt should know the importance of working with facts rather than hunches. That’s why in the following article we’ll throw light on how five emerging roles yield a proven value that companies cannot ignore.
By Kirill Eremenko, Director at SuperDataScience.
It is the heyday for Data Science...
Yes! Yes!Yes!
- 1. The hottest new courses at universities around the world are in the field.
- 2. Data Scientist graduate jobs are listed as having impressive median starting salaries of $93,000.
- 3. Ads for six figure Data Science jobs are commonplace.
Data Science seems to have well earned its title as the “sexiest job of the 21st century.”
But with all this commotion about the field, many wonder whether the trend toward Careers in Data Science is all just a fad. Why hedge your education, career and future on a bet?
It’s reasonable to have these hesitations. All Data Scientists worth their salt should know the importance of working with facts rather than hunches.
That’s why in the following article we’ll throw light on how five emerging roles yield a proven value that companies cannot ignore.
What’s more, we’ll explain the common expectations and responsibilities for each position, to help you focus on securing the job you really want.
Before we look at these five roles in detail, let’s first pin down why data science jobs are so promising.
Why a career in Data Science is worth it?
Data Science roles will grow exponentially
Jobs in our beloved field are en vogue, but don’t let the belief that trends are short-lived dissuade you from learning more about the subject.
So far — So good (source: Indeed.com)
We are living in the Digital Age, and as technology improves so will the ability to capture, store and process data.
Companies need people to manage and control each of these stages. It is therefore a logical step that the need for Data Scientists will only increase as the technological obligations in the world of work become more demanding.
But don’t just take it from me: experts forecast an increase of 364,000 new data and analytics jobs within the next three years. That’s not a number to take lightly.
There is less competition for Data Science jobs
The anticipated 364,000 million new jobs will need personnel to fill them.
Even with the astonishing rise in Data Science courses both online and at traditional institutions, due to its rapid growth it is highly unlikely that the field will be oversubscribed with workers in the next decade.
Companies are struggling to fill roles. Data Science jobs stay open days longer than the more traditional job postings, for instance, it takes a whopping average of 53 days to fill an Analytics Manager position in the professional services industry, that is eight days more than the average of the field.
It is therefore no secret that there is a real lack of suitable candidates, and now is the time to exploit this need in the job market.
The aftershocks of this explosion of data jobs are being felt across industries as diverse as:
- Leisure & Tourism: Airbnb has established an internal university that specializes in Data Science.
- Finance: Artificial intelligence will be used by accountants to reduce the burden of auditing.
- Medicine: IBM has plans to build upon its wildly successful Watson AI and create a cross-disciplinary Data Science network.
In these fields and many more besides, companies need skilled workers to steer them across the choppy waters of data science.
Data science might be your best option for future job security
The idea that robots will one day take over many of the currently available jobs done by humans is no longer the stuff of science fiction, and it should be a serious cause for concern for anyone starting their career.
In an interview with CNBC, Deutsche Bank’s CEO John Cryan stated that, in simplifying work processes through technology, non-technical jobs in the financial world will inevitably suffer.
The Guardian recently reported that four million jobs in the British private sector could be replaced by robots in the next 10 years.
While there are pitifully few jobs that are not at least at some risk of future automation, the draw of Data Science is that it directly analyses, manages and alters the digital backend of work processes and company information.
This makes it a reasonable — if not completely infallible — plan for anyone looking for job security in the uncertain times ahead.
I am pleased to give you a top-level overview of the jobs in Data Science. For a more rigorous excursion into the field, join us in San Diego on November 10–12, 2017 for DataScienceGO.
At this conference, you will get valuable information to help you kick-start your Data Science Career from experts in the field.
Kick-starting your career in Data Science
These three reasons should pique your interest in the field..
But what does a Data Scientist actually do? What is a Data Analyst vs. a Data Scientist?
What options are available for people who don’t enjoy the technical side of Data Science?
Check out the following five key roles and their remits to show that the field isn’t only growing in importance for companies — more, that Data Science is the most invigorating area of work in the 21st century.
Choosing a Data Science job that’s right for you:
There are many career paths from which to choose, and tricky to navigate. To help you find the perfect role, below are the five titles common to most ads, along with a job description of the responsibilities they are likely to entail.
Business Analyst:
These types of roles will be comfortable terrain for many companies, as the concept of business intelligence has been around a long while prior to the data boom.
They are therefore likely to be more competitive than positions that are more analytical or that require programming knowledge.
Business Analysts are unlikely to analyse the data themselves. Rather, they will be expected to turn prepared data into compelling visuals for the company’s future operations.
Whatever the minutiae of your responsibilities may be, strong presentation skills will be important if you are interested in becoming a business analyst.
Data Analyst (Data Preparation):
Specialists in data preparation are truly the backbone of data science projects. Data preparation is no easy task, and a misstep at this stage of the project can result in its failure.
In these positions, workers must trawl through endless rows of data manually, cleaning and structuring wherever required. This makes it a cumbersome and difficult process, requiring some technical know-how and, more importantly, considerable attention to detail. Despite these responsibilities, these roles are typically billed as an entry-level position.
These jobs can be ideal ‘traineeships’ for people who might have taken a course in data science and who want to practice their newfound skills, to increase their confidence in the field before tackling other responsibilities. Prove yourself in this role, and you’ll have a competitive edge over other candidates looking to gain a foothold.
Data Analyst (Modelling)/ Data Modeller:
Although they share a similar name, Data Analysts in modelling are actually responsible for more than their Data Analyst (Data Preparation) counterparts.
Data modellers are tasked with developing systems that can manage and handle company databases. Programming expertise is essential for these roles.
Though data preparation may not be a requirement in the job description, proceed with caution if your skills in preparing datasets are shaky: smaller companies are likely to consolidate Data Analyst roles, which means that you may also be given the responsibilities of Data Preparers in addition to Data Modellers.
Data Scientist/ Advanced Analyst/ Machine Learning (ML) Practitioner/ Senior Data Scientist:
These roles are the beating heart of Data Science. Anyone who wants to tackle them must be a jack-of-all-trades… and master of all! For these positions, you must be well-versed in all the stages of a Data Science project.
It shouldn’t come as any surprise to regular readers that we covet these fascinating roles. These jobs need people to think outside the box in order to solve problems, and then to create actionable solutions for the future health of the company.
Having said that, these positions are only suitable for people who like to take the initiative. If you want to follow a strict set of rules and finish your workday at 5, they are not for you.
If you like a challenge, if you are creative, and if you are hungry for programming and analysis tasks in your job, apply immediately!
Data Science Manager/ Analytics Manager:
These roles get the green light for people who shy away from more technical positions. They only skim the surface of data science — focus is rather on the client and the general management of teams.
Managerial positions are naturally most suitable for people who enjoy communicating across teams and to clients. As they do not directly tackle the technical side of Data Science, they may not be ideal for people looking to become ‘serious’ data scientists.
This is because managers will have their hands full with staff and budget control, and will therefore not have the time to get involved in programming or analysis.
Nevertheless, due to their managerial responsibilities, these positions may be reasonable solutions for those who are already some way into their careers in another field and plan to sidestep into Data Science.
Final Takeaway
When you are looking for a lucrative career in Data Science, keep these breakdowns by your side as a job cheat sheet — it will help you to quickly crop the positions that won’t be a good fit.
Always bear in mind that while we’ve made every effort to highlight general expectations for each role, the actual job specs for each are subject to change from company to company.
As with any other position, always read the spec thoroughly before you apply. Data Scientists are sticklers for detail — make sure that you prove it in your application!
For more information on how to improve your Data Science Career, click here and Join us Live on November 10–12, 2017.
Bio: Kirill Eremenko is Director at SuperDataScience.
Original. Reposted with permission.
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