It is inevitable now that technology will continue to grow and mark its place in all companies of all industries, meaning companies must not only adapt, but innovate. A proactive approach is necessary to move forwards effectively, and the only way to do so would be to recruit talented professionals in technology.
Today, the digital shift of data collection and analysis is becoming more of an obligation rather than a recommendation, particularly within the Finance and Technology sphere. Software Developers in Fintech are already increasing their significance in the banking and finance scene, and with massive amounts of data being gathered and processed, Big Data technology is on the rise, resulting in a demand for professional Data Scientists to clean, organise, extract and analyse large influxes of data to be utilised as valuable sources of industry and market knowledge.
As of present, the shortage of Data Scientists for the increasing demand has made this profession highly valued. Responsible for gathering insights to satisfy particular company needs and goals through the organisation and interpretation of very large quantities of data, this profession is gaining importance as organisations begin to rely more and more on data analytics to fuel their decision-making processes, moving towards AI technology of automation and machine learning in conjunction with Big Data to enhance their operations and strategies.
Worldwide Shortage of Expertise
As of present, the demand for Data Scientists worldwide is projected to exceed supply by over 50% before the end of 2018. Due to this major shortage, the majority of companies particularly within Tech and Financial industries are providing on-the-job training for software developers to lead onto careers as a Data Scientists, meaning that many positions will be filled through trained individuals in Tech, and not through post-graduates in Data Science. Partially the reason for the lack of readily-equipped individuals in Data Science is that this line of career is professionally particular in the sense that a Data Scientist possesses skills of expert data analysis and software programming which must be matched with a strong sense of business intuition. Therefore, with graduates in software development possessing the required computational expertise, these individuals are normally given a formal training course by the company they are employed at to better prepare them to step up to a role in Data Science within the same company.
Fortunately, the educational systems around the world are introducing proper courses to prepare prospective Data Scientists for their career without the need of intense on-the-job training. However, it will take time for such individuals to meet the shortage of professionals in this field as reading for their higher degrees will take the necessary time. Meanwhile, companies will continue to make use of the current talent pool in technology to address and satisfy the roles required through private training and enhanced team collaboration through which work related to data analysis, data management, AI and Big Data software development and business strategy would be distributed according to the expertise available at the company.
The Skills, Roles & Responsibilities of the Complete Data Scientist
Data Scientists are avid problem solvers keen on making new discoveries amidst large volumes of data in real-time through machine learning and pattern recognition software development. Critical thinking skills are first and foremost of great importance in this profession, by deploying skills of mathematics, statistics and coding to develop key business strategies. That being said, such professionals must be proficient in statistical programming languages of R and Python and database query languages of SQL. Software Engineer and data manipulation skills allow for well-prepared Data Scientists to handle data-logging and develop data-driven products. Knowledge in Big Data, Artificial Intelligence, machine learning and deep learning are all valuable assets especially in this digital age, and being able to develop complex data architectural frameworks will aid business decision making in the long run.
A well-balanced mix of hard skills and soft skills for Data Scientists to possess are favoured by employers. With programming seen to being the most favoured, it makes sense that a Data Scientist’s programming skills are required to be able to analyse large sets of data and develop personalised data tools. To complement such a skill, understanding the respective company’s product offerings will help Data Scientists perform quantitative analysis which is also an important skills for running experimental data analysis and scaling data strategies. Soft skills would include exceptional interpersonal, communication and team-playing skills in order to effectively share and understand knowledge, outline goals and execute tasks.
In terms of education, Data Scientists usually obtain a Master’s Degree or higher in Computer Science or Data Analytics, and then develop business-skills as they practice their profession within a particular industry. Technical expertise is not limited to computational and programming knowledge, as the Data Scientist processes this knowledge to make sense within a business environment. This means that a valuable Data Scientist is one who is as business-minded as they are computer-minded. So much so, that a successful Data Scientists assists a company in their risk-management to make improvements in the company’s processes when dealing with clients and customers.
The shortage of talent in this profession paired with the great importance and responsibility which comes with the role means that professionals in this field are unsurprisingly highly rewarded - tapping into the complex world of Data Science proves attractive for individuals looking to head for a prospective career in technology, and now is the time for them to do so.
The balance of attributes of intelligence and intuition are put at the forefront when regarding a professional and talented Data Scientist, as the intelligence needed to handle and understand complex data structures must be complemented by the excellent ability to implement such data into effective business models which will improve security, enhance strategies and increase revenue for the company they are working for.
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