So you want to be a data scientist?

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Data scientist has been billed as the ‘sexiest job of the 21st Century’ – but who are data scientists, and how can you get in on the action?

iStockphoto/Thinkstock

iStockphoto/Thinkstock

Guest post by Michael Koploy of SoftwareAdvice.com

By now, pretty much everyone has heard that “Big Data” will be the next “big thing” to revolutionise how we work, live and communicate.

But who will manage the Walmart database that contains over 2.5 petabytes of data from the retailer’s 1 million customer transactions per hour? Who at YouTube is analyzing the 48 hours of video uploaded to its website every minute?

For every Big Data problem, the solution often rests on the shoulders of a data scientist. The role of the data scientist is similar in responsibility to the Wall Street “quants” of the 80s and 90s – now, these data experienced are tasked with the management of databases previously thought too hard to handle, and too unstructured to derive any value.

The sexiest job of the 21st Century?

Thomas Davenport and D.J. Patil brought the data scientist into the national spotlight in their October 2012 Harvard Business Review article: Data Scientist: The Sexiest Job of the 21st Century. Job trends data from Indeed.com confirms the rise in popularity for the position, showing that the number of job postings for data scientist positions increased by 15,000% between the summers of 2011 and 2012.

When I asked Bruno Aziza last year how he would best describe a data scientist, his answer still sticks with me today. “Think of a data scientist more like the business analyst-plus,” he told me. Part mathematician, part business strategist, these statistical savants are able to apply their background in mathematics to help companies tame their data dragons. But these individuals aren’t just math geeks, per se.

“A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.” — Anjul Bhambhri, Vice President of Big Data Products, IBM

Tips for aspiring data scientists

If this sounds like you, the good news is demand for data scientists is far outstripping supply. Nonetheless, with the rising popularity of the data scientist – not to mention the highly-competitive companies that are typically hiring for these positions – candidates will have to be at the top of their fields to get the jobs. Here are some quick tips for career success.

For students about to graduate: Focus on academics before (and after) graduation. Successful data scientists come from a number of different disciplines: biostatistics, econometrics, engineering, computer science, physics, applied mathematics, statistics, machine learning, and other interrelated disciplines. Experience applying the scientific method to many disciplines and areas of research will provide fruitful in the field of data science. And as important as academics are during school, it’s just as important to stay up-to-date with current research trends and discoveries within academia, even after graduation, for instance by subscribing to academic journals.

For career entrants: Focus on business acumen. While programming and statistical expertise is the foundation for any data scientist, a strong background in business and strategy can help jettison a younger scientist’s career to the next level.

Krishna Gopinathan, founder of Global Analytics Holdings, recently recounted to me how he has built some of his most exceptional data scientist teams. The secret, in his opinion, was to build teams around data scientists that ask the most questions about:

  • How the business works
  • How it collects its data
  • How it intends to use this data
  • What it hopes to achieve from these analyses.

These questions were important to Gopinathan because data scientists will often unearth information that can “reshape an entire company.” Obtaining a better understanding of the business’ underpinnings not only directs the data scientist’s research, but helps them present the findings and communicate with the less-analytical executives within the organisation.

While it’s important to understand your own business, learning about the successes of other corporations will help a data scientist in their current job–and the next.

Types of companies hiring data scientists

Luckily, the answer is: everyone and anyone is looking for a data scientist.

This role is in high-demand within some of the most attractive fields and industries, including government agencies, high technology companies, and consulting & market research firms. For a taste, I’ve shared seven recent job postings for those interested in any of these fields.

Government Agencies. The Consumer Financial Protection Bureau is looking for a data scientist to work directly with the Chief Data Officer and help the establish the bureau’s direction while leading a team of other scientists. Meanwhile, the CIA is looking to hire an entry-level data scientist to develop new algorithms and find relationships within the Agency’s existing database.

Technology Companies. LivingSocial is hiring a data scientist to help drive the company’s strategy as it analyzes data from its 70 million customers worldwide. EMC is looking to add a senior data scientist to its San Mateo office to work in its Greenplum Big Data analytics division.

Consulting & Research Firms. Booz Allen Hamilton is looking for a data scientist to help manage its unstructured database, to help identify new insights for its clients. SymphonyIRI would like to hire a data scientist to increase its understanding of customer behavior, category insights and market trends to better assist its CPG and retail clients.

Scientific Organisations. The National Institutes of Health (NIH) are looking for an Associate Director for Data Science to help deal with the huge amouts of biomedical data they generate.

In conclusion, no matter which field you pick, the good news in this:  data science is in its infancy, and its outlook seems golden as our lives and data become further entrenched.

Follow up:

You can read our follow up post on data science here.

Further reading:

For more data scientist career advice, check out some of the additional resources below:

Michael Koploy is an Analyst and Managing Editor for SoftwareAdvice.com, a company that provides software buyers access to free research, price quotes and demos. Koploy’s research focuses on business intelligence and analytics applications, particularly those within the retail and distribution industries. You can reach him on linkedin.com/in/michaelkoploy or direct at michael@softwareadvice.com.

44 thoughts on “So you want to be a data scientist?”

  1. Really interesting post. Definitely agree that data scientists need to ensure that they don’t just have the mathematical skills, but also need to have the business knowledge to be able to answer the question “what does this mean?”. Having the skills to make actionable insight with real advantages for business/organisations is really important.

    Imperial College London has just launched a new Master’s programme, MSc Data Science & Management, to answer this need. You can find out more here: https://www3.imperial.ac.uk/business-school/programmes/msc-data-science

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  2. Data scientist is a relatively new field. Health Research Analysis , preceded the ‘big boom’ in ‘data mining’ , data science , by quite a few years. A mere decade ago you could search Health Research Analyst and you would get me. Now , you can’t even find me. I still have people asking me how I claim to be a ‘Health Research Analyst’ and I explain how I coined the term , and leave it at that , unless they press my claim.

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  3. I completely agree- believe someone who understands or can pick up a business context will have the edge- the statistical knowledge and technical wizardry can be picked up. I hate it when data science is seen as a game of advanced statistics and the theory that tons of data and algorithms can make up for subject matter expertise

    I feel quite strongly on this and mention the same on my blog bizmachinelearning

    https://bizmachinelearning.blogspot.in/2015/04/what-makes-for-ideal-data-scientist.html

    Reply ↓

  4. Developing statistical models or algorithms (i.e. knowledge) and providing inside details to the world for the betterment of society is a key challenge. I am a data scientist, because I enjoy playing with Data i.e. big or medium or small data will not change my passion to enjoy playing numbers and statistical algorithms.

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  5. What is missing the most, in my opinion, is practice. Sure, one can manage to find a dataset and tinker with it, but this is rather unstructured and therefore not the best in terms of learning efficiency. It would be lovely to have something like Kaggle, without the competitive component though, oriented at budding Data Scientists; so that people learn together, not separately. Showing each other’s code, answering each other’s questions and so forth. A sort of sandbox to tinker with the technology in a more controlled environment. Try this website for more info https://activewizards.com/

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