(Editor’s note: Today Information Management launches a weekly series of articles looking at the top jobs in data science and analytics, beginning with what has been referred to as the sexiest job in America – the data scientist).
Job title: Data scientist
Reports to: Depending on the organizational structure of the business in which you as a data scientist find yourself in, you may be reporting into a lead data scientist, a principal, a chief technology officer, a chief data officer or in cases of some start up organizations, perhaps a CEO.
As with your responsibilities, the organizational structure around you will be dictated by the needs of that company, and there is no real universal pattern when talking about who data scientists would report into. You may work individually, as part of a team of other data scientists, or in many examples, as part of a wider software engineering team, again depending on the aim of that company or data science function.
Demand for this role: Having been widely considered the “number one technology job” and “America’s most sexy occupation” for the past two years, the data scientist really is at the forefront of technology, and with over three times more advertised opportunities than qualified candidates, there quite simply is no other set of abilities in such higher demand.
Top industries hiring for this job: Data science is revolutionizing every industry, from finance to healthcare, media to advertising, the start-up world to global corporates and everything in between, and it’s no surprise. The value added to any business that data science can bring is immeasurable, and it’s certainly an exciting area of technology to be involved with.
Responsibilities with this job: A typical data scientist will be an amalgamation of the ability to build and engineer machine learning models while applying advanced mathematics or statistics. It is this combination of the engineering and statistics that separates the data scientist from a software engineer and a statistician respectively.
Required background for this job: Within the data science space, there is often a specific set of requirements, both academic and technology-wise, that most data scientists will universally have and use on a day-to-day basis. Academia is key for this area, with most companies look for a minimum of a Master’s degree in a quantitative field, such as but not limited to computer science, physics, mathematics and statistics.
Many employers will only consider candidates with a PhD, however this trend in hiring is slowly fading out and the importance of a PhD is becoming less so. Regarding technologies, Python and R are far and above the most popular and in-demand technologies for top data scientists. Many organizations also look for strong C++ skills as part of a candidate’s portfolio, while exposure to big data technologies such as Hive, Hadoop and Spark are always a plus but not always necessary.
Skills requires for this job (technical, business and personal): On the ‘softer’ side, successful data scientists need to be passionate and forward-thinking, and an interest in research is often a sticking point for a lot of businesses hiring for these types of candidates.
Data scientists should be always looking for new ways of approaching tasks or business issues and exploring emerging technologies. Many organizations will look for code examples, such as GitHub or StackOverflow profiles or publications, as well as an updated resume, so a strong online profile and recorded projects will add huge weight to any job applications when applying for positions in this space.
Compensation potential for this job: Starting salaries for a fresh PhD or Master’s degree candidate can fetch $110,000 to $120,000 per year in New York City, and salaries of $200,000-plus are not unheard of for strong data scientists with anywhere between 5 to 8 years’ worth of experience.
Success in this role defined by: Data science can be applied to multiple industries in a wide range of ways for different purposes, so a data scientist’s role or responsibilities will differ immensely depending on the industry. Take Investment banking, for example. A data scientist may be hired to build machine learning models to predict potential investment targets for large financial reward, while a data scientist in the pharmaceutical space may be tasked with predicating any new successful drug discoveries to fight disease, which in turn would be different to a data scientist predicting the success of a marketing camping working for an AdTech business.
Advancement opportunities for this job: The potential of data science across every industry is unprecedented, and the role of candidates in this space can differ drastically and reap rewards in multiple different ways. The general role of a data scientist will, to some degree, be similar in each industry, i.e. building machine learning models for predictive analytics. However, the way in which that model is applied to each business will be hugely dependent on the industry and aim of the organization.
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