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Fastest-Growing Job Opportunities In Data Science And How To Work Towards Them

Updated: Jul 18, 2019

Data Science has shifted from just a buzzword to a major element in organizations. This field is experiencing a significant overall growth in jobs in the hiring end. In fact, data scientists are rapidly taking on more strategic positions as organizations adopt a product-centric understanding of data. It's a sector that offers massive job development and greater earning potential. A recent study says 97,000 job opportunities are available in this field.

Job opportunities are increasing in Data Science

This rapid development in the sector has resulted to a new trend–job roles are becoming more standardized, well defined recruitment conditions are in place, and fresh job roles and designations are coming to the surface. For instance, we see opportunities for Business Analyst and Machine Learning Engineer, or a Deep Learning Engineer where the job description for each of the profiles may appear to be overlapping but the career path and compensation will completely different for each.

In fact new job titles also results in the growth of employability of an organization. While some of these work positions may have some overlapping duties, some evident differences also exist. Data Science today offers plenty of chance for non-STEM (Science, Technology, Engineering and Math) majors lacking a background in programming to join the IT sector or any other sector where data analysis plays a strategic role.

We dive deep into the rapidly increasing job positions with an opportunity to join this fascinating arena, given this fast rate of growth. In order to get a definite perspective of the new positions, we need to know how data science, which has always existed as a statistical analysis, has now developed into a complex business task and has created positions that involve a blend of abilities.

Some of the fastest-growing Job opportunities & how to work towards them

1. Data Scientist

There is a huge demand for data scientists across the field, ranging from big companies to startups and e-commerce industries. The industry is gradually moving towards a full-stack data scientist, someone who can also operate on a particular application area, such as NLP or Computer Vision, and develop industry-leading data-driven products. Given the increasing need for skill in the space, more programs on data mining, programming, analytics, etc. are now being introduced.

Key skills: Experience in predictive modeling and analysis using machine learning techniques or algorithms, analytical methods such as Regression, Classification, Clustering and Time Series are chosen for Data Scientist positions. Python has emerged fast as Data Scientists ' most in-demand expertise.

Career Path: Although the profession is filled with MOOCs, what is needed is an extensive training program that provides a very well-defined, updated course and opportunities for placement. In addition to a basic knowledge of machine learning techniques, Data Science courses provided in George Institute of Data Science , Kolkata offers thorough, hands-on training in predictive modeling, R, Python.

2. Machine Learning Engineer

Machine learning engineers, working with huge volumes of data, have become the foundation of consumer facing technology firms. ML Engineers are responsible for developing the application design and automating the model training, process and implementation method to guarantee a constant supply chain. In short, ML Engineers make sure that models and pipelines for machine learning are put into production.

Key skills: ML Engineers have in-depth knowledge of data structures, algorithms and object-oriented programming. They also have experience in machine learning techniques, like decision trees, clustering, regression and neural networks. Their competencies include data and feature engineering, structured and unstructured mining.

Career Path: Machine learning engineers need technical background and knowledge of traditional ML techniques. Our machine learning programs offers a thorough knowledge of frameworks, Tensor Flow and how neural networks can be built. Applicants can use relevant case studies on computer vision, data processing, and image processing to evaluate their skills.

3. Business Analyst/Data Analyst

Business Analyst knows the business and data requirements, transforms the client's operational requirements, has deep domain knowledge, and can also collect and report data. The key skills needed are the combination of analytical and functional abilities to move projects forward and in-depth knowledge of how business analysis provides organizations with greater significance.

The major difference between Business Analyst and Data Analyst is that DA works with big data sets, has a programming and analytics expertise, while BA works more on the business end, collecting and analyzing information, knowing the nature of the task, and making efficient choices. It takes different capabilities for the two positions.

Key Skills: Excel, SQL, Tableau to advanced tools like SAS, R, Python, Data Science

Career Path: Since the task of BA / DA depends highly on reporting, training in Analytics & Data Visualization offers a well-defined professional route for both Business Analyst and Data Analyst roles.


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