Data scientist job is the hottest profession in the market right now. A lot of demand for these professionals is bursting out there. With the captivating buzz around the corner, data science is underpinned with almost every sector now. Be it healthcare or education, the data-driven technology is ticked in every single cubicle.

But what about the finance industry? Do the trained statisticians with postgraduate degrees and great mathematical knowledge makeup space for data scientists to venture in to? Well, living up in 4.0 era, you must have inculcated data-operations into your nature by now.

When there is data, there is a data scientist. Irrespective of field, sector or industry, every data-driven station has a vacancy for the data scientist to enter.

However, professionals of applied statistics do differ from data scientists in terms of practical experience and knowledge. The commercial focus of data scientist surely variates from that of a statistician. Or in other words, we can say, that data scientists are new age statistician with an influence.

Let’s understand the attributes data scientist should possess while approaching for finance industry.

•  The education qualification of the candidate should hold a first-class degree in mathematics/statistics, computer science, physics, engineering or other subjects with vital mathematical content.

•  Skilfulness in multiple programming languages (both compiled and interpreted) including C/C++, S/R, Matlab, Python or Java is quintessential for the candidate.

•  Additionally, well-versed database skills including SQL programming in MySQL, PostgreSQL, Oracle, SQL Server, and other RDBMS programming can be cited as an extra edge for the person.

•  The professional with artistry with handling time series data from Bloomberg, Reuters or other multitude financial data streams available, can stand out in the crowd for sure.

Other Less Discussed Characteristics To Be Considered:

•  A candidate needs to have the ability to communicate mathematical ideas well both verbally and visually to non-specialists.

•  He/she should know how to harness mathematical training to solve genuine commercial problems.

•  A person with a good understanding of optimization along with solid linear algebra and calculus (school-level) of statistical inference, simulation, multivariate analysis, and proper data visualization will be considered efficient for the job.

•  Candidate possessing above basic knowledge can easily adopt support vector machines, neural networks, random forests, and gradient boosting and NLP techniques.