With the evolving data science landscape, organisations are expecting more from data scientists. Consequently, various companies are seeking skills in data scientists that were ignored a few years ago. And, therefore, aspirants should devise a data science resume that aligns with the latest needs of the enterprise to increase their chance of getting job offers. Failing to do so can drastically decrease the likelihood of data scientists to stay relevant or differentiate themselves from others, resulting in losing opportunities.
Today, data scientists should not confine their data science resumes only with fancy tools, ML and DL techniques, and certifications. Instead, they must include skills that can demonstrate their capabilities that are now a prime focus for organisations.
Work On Specific Projects
Projects are probably one of the essential aspects, which firms are seeking in candidates to assimilate their abilities. However, in this day and age, projects that are already present on the internet, and do not portray the unique value proposition. Aspiring data scientists mostly do projects keeping in mind that it will help in communicating the skills and techniques they can apply on different use cases. Instead, projects are about solving business challenges, thus utilising the same data sets that are available for years cannot showcase data scientists’ proficiency.
“Data scientists should analyse business models of various companies, create dummy data and work on solving the problems,” said Bastin Robin, Chief Data Scientist at CleverInsight. “For one, data scientists can evaluate the problems of Bounce — a self-drive bike rental firm that is allowing users to keep bikes in the vehicle at home. One can think about how it can be optimised so that the vehicles continuously move and the company can make more revenue.” For this, data scientists can make dummy data sets of the ride and try to solve the challenges.
However, Robin also suggested that if one wants to work on projects that are already present over the internet, they should strive to increase the accuracy of the model using various techniques. Achieving a higher efficiency with AI models does help in catching the eye of recruiters.
Converting Business Problems Into Data Science Problems
Practising the above methodology enables data scientists to translate business problems into data science problems. Still, it varies in the fact that one needs to have the ability to do it quickly. “Often data scientists are given the business challenges and are asked to break down the problems in small data science problems,” says Santosh Rai, AI architect and a head data scientist at ProVise Consulting. “Data is not always readily available. Thus data scientists need to collect data according to challenges and solve business issues.”
While delivering results using data science techniques is critical for making informed decisions, the ability to make one understand the ins and outs of the outcome is vital. Demystifying the black box is essential; thus, data scientists should be able to pinpoint the factors that lead to specific outcomes.
Explainability is taking centre stage in organisations as purchasers today ask for clarity in the results that products deliver. Mentioning the reasons that led to the outcomes in their projects is highly recommended as will demonstrate your capability of strong basics and advanced statistics along with data science knowledge.
Unlike other developers, data scientists make decisions that can directly impact businesses. Therefore, it is essential to consider critical factors while making decisions to avoid the adverse effects on business operations, which can result in loss of revenue for organisations. Having a business understanding is of paramount importance. To ensure you communicate this to the recruiters by adding how your outcome of projects can help in decision-making, thereby helping in business growth.
Going one step ahead with unique skills can result in gaining an advantage. This can be achieved by utilising various platforms to communicate your skills and work in the community. “Writing is the most underrated skill in data science,” said Parul Pandey, a data science evangelist at H2O.ai. She stresses on the fact that the system of creating resumes will go obsolete in the future, and that’s when writing blogs to communicate work will be the trend for getting the attention of recruiters. Consequently, data scientists should leverage the potential of platforms like Medium and LinkedIn to put their work out.