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Recruitment | 10 Min Read

Hire a Future Ready Data Scientist

Introduction

Ever wondered how Netflix can decide on which original series to produce? Or how Grab can gain a better understanding of the problems they are meant to solve? Well, they owe it all to their data capabilities. Data is the new corporate currency. The insights garnered are used by companies to get a holistic view of the information that can be filtered and utilized as per their need. This helps them resonate with their target audiences, carve a distinct position, and outlast the competition.

However, there is much more to data than we can fathom. So let’s first understand the science behind data.

Traditionally, data was minuscule, easy to manage and structured before the advent of technology. As we began leveraging technology and smart products, data could no longer be maintained with a simple SQL database (Do refer to our insightful blog dedicated to Database Management). This raw data was heterogeneous. It was unstructured, vast, complex, and extremely advanced as it originated from multiple sources, such as the internet, smartphones, and online transactions from multiple timelines and geographies. This led to an urgent need to streamline and store it safely.

Predictive analytics soon suggested an unprecedented growth of information, to be generated worldwide on the internet. Hence, the focus shifted towards extracting knowledge from the stored data. Experts believed that by deep-diving into the collected data, one could gain granular insights into complex behaviors, trends, and inferences. This could help companies to make forward-thinking business decisions. However, the complexities associated with the vast amounts of data created a challenge that could only be addressed with intelligent data capture techniques. These techniques needed a scientific approach to cleanse, mine, and align data for harnessing its potential.

Data Science and the Role of Data Scientist?

Do you know: every data-set tells a story. But considering the influx of information at our fingertips, it is challenging to understand which ones are vital unless we uncover findings? Over 2.5 quintillion bytes of data are created daily. Hence, data science came into play. Data Science is an exciting blend of various tools, algorithms, statistics, mathematics, and machine learning principles to uncover and articulate hidden insights from raw data. It shapes the future with its accurate predictions. By generating robust, actionable analytics from large data sets, Data Science brings clarity to the audiences’ needs. How else do you think behemoths, such as Facebook, Google, Amazon, create appropriate personalized recommendations for their audiences? They utilize every information coming their way. This includes, web server logs, tweet streams, search terms, voice samples, online transaction records, product reviews, or any other source, and draw viable conclusions from them, thereby enhancing the customer experience.  

What is the Role of Data Scientist?

As data scientists, our job is to extract a signal from the noise.”
Daniel Tunkelang
, Consultant / Advisor

While Data Science may seem entirely automotive and mechanical, a data scientist plays a significant role in harnessing the potential of data. They are multi-talented professionals with core knowledge of programming, statistics, prediction analytics, algorithms, and whatnot. They mold a combination of their skills into a suitable data story that aids decision-making in real-time as well as sets a course of the future. No wonder they are the backbone of any data-intensive company. And as long as our dependence on data lingers, the demand for someone to manage it will continue. We don’t intend to jump the gun, but Data Scientists are the future of the world. As per Glassdoor’s 50 Best Jobs in America report, it ranks amongst the best job industries.

Why Hire and How to Hire a Data Scientist?

This is as good as asking why data science is important? From empowering decision-making, delivering solutions for a specific problem, ensuring customer retention to mitigating vulnerabilities and fraud; the profile of a data scientist is exhausting. While data has the power to fulfill the ever-increasing demand of the present and the future, data scientists reshape the ecosystem and modernize evolution by leveraging it. And that’s not it; Data Scientists also helps organizations understand the impact of their decisions. They measure the critical metrics related to significant changes and quantify a company’s success.

How to Attract Data Scientists to your Organization?

The Great Learning analysis revealed that sectors have been facing a huge skill shortage in the country. In 2019, as per the study, around 97,000 positions related to analytics and data were vacant.

It was due to the lack of qualified talent. This means if you are someone planning to move towards this profession, it will be interestingly competitive even though few are exploiting the opportunities. At the same time, if you are looking to recruit a data scientist, the task is equally challenging. According to Firstround.com, strong candidates in Data Science, often receive 3 or more offers, so the success rates of hiring are typically below 50%. Hence it is important to apprise yourself with the exact profile for which you need a data scientist. Whether it is a freelance data scientist, you are looking for a full-time employee, begin with understanding the life-cycle of data science.

The Life-Cycle and Skills of a Data Scientist?

life-cycle_of_Data_Science

What Are the Skills Needed for Data Science?

Working with data can get a bit messy and if a candidate is not skilled enough, they might present insignificant insights. Hence, as a hirer or decision-maker, you need to identify the ideal skills set required by a prospective candidate. Data Scientists have to be an exceptional combination of industry-specific knowledge and soft skills that will explain highly technical results to their non-technical counterparts. They should be one of the finest in analyzing and interpreting data. After all, the hiring process will be designed around challenging the candidate in every possible area to measure their standing. Here’s a look at some of the most preferred skills required in a data scientist:

Trends in Data Science for 2020

Over the last couple of years, we’ve come to acknowledge that data will remain omnipresent. We’ve also realized that the smart algorithm of Data Science has come a long way in understanding the working of various industries. As a result, it can now be applied to almost every kind of organization- travel, healthcare, retail, manufacturing, and education. Hence, with a manifold increase in demand across incredibly dynamic categories, we need to be updated with its latest offerings to pave the path for a better future. 

To keep you updated with the latest trends in data science, here is a list of top 5 data science trends that are ubiquitous across all industries and are bound to push your business to achieve great success:

Growth of Data Science Roles in 2020

As we migrate towards complete digitization, Data Science is going to be a game-changer across every industry. This engineered solution with the ability to solve problems for companies, way faster than before, has placed it on a steady mantle. As a career choice, it is expected to escalate, this year by a considerable margin, across the globe. As per a Forbes report- the job requirements for data science and analytics in 2020 are projected to surge to 2,720,000 openings. The coveted U.S. Bureau of Labor Statistics claims that 11.5 million new jobs will be created by 2026. Freelance data science jobs are also expected to grow. Companies will be able to hire freelance data scientists for a particular project or a specific time. Hence, our increased affinity for data science to foster technological advancements will inevitably create the potential for data scientists' jobs in every field.

Hyper- Automation in Data Science

According to eminent research by Gartner Group, nearly 85 percent of big data projects fail due to its complex and interdisciplinary nature. With automation, data scientists can make significant inroads to make the Data Science process less challenging. Data scientists get assistance in discovering the 'unknown' scenarios quickly by taking AI and machine learning benefits to the next-level. Even the 'Citizen' data scientists will be able to build a data-driven culture to streamline their companies without excellent technical skills.

Evolution of Big Data in the Data Science Landscape

Big data and Data science are somewhat intertwined since Data Science is about leveraging Big Data, an essential resource for many. As we move to stream humongous amounts of raw data (IDC forecasted the global data sphere would reach 175 zettabytes by 2025 ), the contribution of data science in deciphering globally relevant events will become crucial. It will alter the quality of life. With large-scale data transparency and related information, communities will be able to put contingency plans into action. This will include real-time forecasts of climatic events that'll help fight climate change, understanding seismic sensors, investigating deforestation, and connecting independent data sources to reveal the trade flows for commodities.

Edge Computing

Till now, the cloud computing trend of accessing virtually limitless processing power and storage capacity was the mainstream process. Now, boosting the traditional model is edge-computing architecture. As per Grand View Research, Edge Computing market size is expected to be worth $43.4 Billion By 2027. Edge-computing is driven by everything that's beyond the internet. It is a continually streaming data which is stored close to the data source for real-time interpretation. This reduces latency and increases speed, connectivity, and bandwidth. This will allow computation and data processing power to gradually move towards edge devices to gain a scalable real-time analysis.

Natural Language Processing

NLP has offered huge breakthroughs in Data science by extracting insights from 'text' like documents, spreadsheets, audio recordings, emails, to name a few. With the expansion of multi-channel data, NLP is also growing exponentially in interpreting speech-recognition applications. The ability to communicate with humans in their language will help businesses go beyond customer actions and analyze customer sentiments. Individuals will be able to access, analyze, and leverage data more intelligently. NLP has already entered the mainstream by merging conversational engagements with technology. It is driving a positive change and will be visible across industries fostering an enjoyable customer service with long-term loyalty. The advancements of NLP is directly responsible for the unprecedented growth of Data Science. A 2017 Tractica report estimated the 2025 NLP market, including hardware, applications, and services, to be around USD 22.3 billion.

Career Path for Data Scientist

People often ask whether being a data scientist is a stressful job. Is data science a tedious job? It is a non-negotiable fact that Data Scientists have become a vital part of numerous organizations. Whether it’s major corporations, e-commerce industries, or start-ups, each is scouting for a data scientist who is cohesive and collaborative in lending strength to their businesses. 

Whether you are an aspiring Data Scientist, a freelance Data Scientist, or a professional Data Scientist, the expectations are high.

Here's how you can carve your space:

  • Don't restrain yourself to one industry. Research your area of interest/ sector, hone your skills set, and dominate the industry. For example, the financial sector or the banking sector can be your domain to showcase your prowess in data security and privacy.
  • While a high-level of academic training isn't regarded as compulsory by many organizations, it does pay to proactively upskill yourself via professional development certification courses, online classes, and boot camps. It helps you stay abreast of the latest technologies and trends.
  • A solid background in data analytics and business intelligence skills can help embellish your experience and advance you towards a lucrative career.

How do you know if data science is for you? You can identify various data science profiles and analyze the opportunities that meet your preference and match your talent.

Data Scientist

Skills Needed

For Programming (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning.

 

Data Analyst

Skills Needed

For Programming (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization.

 

Data Engineer

Skills Needed

For Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)

 

Business Intelligence Developer

Skills Needed

For Programming languages (VB, C#, JavaScript), SQL databases (Relational database, Data Warehouse), Power BI, DAX, and security rules, and SSRS/SSAS/SSIS, ETL (Extract, Transform, Load), Report Builder.

 

How Mercer | Mettl Helps Hire the Best

The most significant filter in your recruitment process is a hands-on programming assessment test.

Hence, Mercer | Mettl’s automated online data science assessment tests simplify your technical hiring process. A data science assessment test identifies potential candidates, evaluates them on various skill sets and provides a comprehensive summary of their knowledge. It is in tandem with the company’s vision of what a data scientist is required to do regularly. 

We also provide diverse data science testing tools and test cases, such as question types, including MCQs, case study simulators, coding simulators, etc. With a data-driven evaluation methodology, intuitive user interface and customized reports, recruit the best data scientist, whether through campus drives or resumes, within the stipulated timelines.

Ready-To-Use Pre-Build Tests

The quality and coverage of our technical questions are unmatched as we cover a vast array of roles and skills. A rich repository of 100,000+ Technical Questions for 300+ Skills helps test a coder’s competency at every level comprehensively. You can choose from a set of readily-available standardized tests, such as  Data Analyst test, Online Data Interpretation Skills Test, Online Big Data Hadoop Test, Blockchain Quiz, to name a few, from our vast library of Programming Assessments. With a ready-to-use, pre-build test set, instantaneously assessing a coder’s skill helps save precious time.

Customization at Each Level

In case you are looking for something specific, our team of experts builds a customized assessment that you can’t find anywhere. These tailor-made pre-hiring assessments are an ideal filtration process to explore candidates’ desired skills set without bias, not only for IQ skills but for coding skills. Tailored solutions matching organizational objectives with features and applications are offered at every level to assist you in hiring an ideal data scientist.

An Extensive List of Simulators

Mercer | Mettl offers an extensive list of simulators to evaluate a coder’s hands-on experience and ability for the desired role. They enable hirers to give a real project to a coder from a specific domain of software development. They also evaluate their experience and capability to solve a problem in any desired programming language. These real-world challenges give a real sense of a candidate’s project building capabilities.

This comprehensive simulator environment is available for

  • Codelysis: Back-end Simulator

Evaluates candidates’ experience and capability to solve a problem in any desired programming language

  • DBLysis: Database Simulator

Evaluates candidates’ ability to write correct and optimized SQL queries

  • FES: Frontend Simulator

Evaluates candidates’ capability to implement web designs and solve problems using front-end technologies

  • R: Data Science Simulator

Evaluates candidates’ analytical & statistical acumen for data analytics roles

  • Code Project: Backend+DB Simulator

Evaluates candidates’ ability to develop console-based applications

  • MEAN Simulator

Evaluates candidates’ ability to develop front-end, back-end and database applications using JavaScript.

These simulators are highly-scalable, auto-graded and designed to test students’ and working professionals’ coding skills. Our simulators support all popular technologies and enable creating language-agnostic questions. A complete auto-graded evaluation report provides a detailed code journey with a plagiarism detector.

Unique Role-Based Coding Platform

If you have an advanced requirement to evaluate candidates in a project-based coding environment, Mercer | Mettl is introducing a first-of-its-kind, all-in-one coding assessment platform to assess candidates on project-based, real-world tasks. This highly sophisticated and customizable tool with real-time, insightful reports and auto-evaluation of the code provides best-in-class integrated development environments (IDE) and programming tools to evaluate their proficiency levels. It caters to all major programming roles and supports the most diverse set of technologies. Major roles include:

  • Front-end Development
  • Back-end Development
  • Full-Stack Development
  • Data Science
  • DevOps
  • Quality Assurance

The Future of Data Science

Look ten years into the future, and data science looks bright and lively as ever. The urge to derive radical insights from large data-sets to gain a lasting edge is going to be one of the key factors fostering the growth of a myriad of data science platforms in the coming years. It is true that not only is it on its course to becoming a coveted job for the decade, but it is also going to be a game-changer, surging way ahead of its time. The emergence of new data science paradigms will make its proliferation endless. Machine learning” (AutoML) will automate a significant number of tasks for data scientists leaving them with ample time to concentrate their energies onto something else. This will open doors for innovations like “Deep Learning” by allowing for accurate predictions and decision making. Inevitably our dependence on codes will also decrease, and so will our methods of trial and error. Data Scientists would bid adieu to complex software repositories and transform their insights into a more strategic and creative plan. 

An influx of sensors, robots, and robust data analytics across both manufacturing and service industries, will see the emergence of technologies that have the power to drive a whole new cycle of global economic activity. Do not underestimate the change ahead of us. 

Understand that Data Science is as much a part of the present as it is of our future. It is evolving at a pace wherein our future will depend entirely on adopting disruptive technologies to remain innovative and agile. Every business’s focus has to shift towards easy methods to drive efficient processes and address growing needs. Being identified as a future-thinking pro-technology business will grant us more cache among investors, customers, employees. Since we are at the cusp of a Fourth Industrial Revolution, it will not be the big fish that eats the small fish; it will boil down to the fast fish eating the slow fish. The elevated level of agility, automation, and the creation of analytical pipelines that are the crux of Data Science will become prescriptive with artificial intelligence (AI) and machine learning (ML) tools. It will ensure businesses adopting Data Science are a part of something larger than life.

Originally published July 3 2020, Updated July 8 2020

Shirisha Jain

Written by

Shirisha has been helping countless brands gain traction with her content. Her deep understanding of the education sector and sound knowledge of technical skills have helped her structure the most creative solutions for key stakeholders. Shirisha has also ghosted pieces for several industry honcho’s successfully published both online and offline. When she's not keeping up with the world, you're sure to find her catching up on bollywood stories or gramming for fun.

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