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6 Job Profiles That Can Pave Way To A Data Scientist Role

6 Job Profiles That Can Pave Way To A Data Scientist Role

Richa Bhatia

While there is already enough narrative out there on the kind of skills that goes into the making of a data scientist, there is very little information on the job profiles that can lead to Data Scientist job roles.  Hailed as the sexiest job of 21st century, data scientist job title has attracted too much attention over the last few years, yet employers find it tough to make a match for this job role.

With more and more companies becoming digital and data science startups flush with VC money mushrooming up, analytics related jobs are on a definite rise. But the talent pool of data analysts and data scientists has stayed small. Sometimes, it is hard to find the perfect match of data science and analytics competencies for hiring these data-driven decision makers.

And it’s not just analytical-enabled and machine learning skills that can land a top spot, domain knowledge, exceptional visualization skills, curating data (wrangling and cleaning), knowledge of data governance ethics also fit the recruiter’s description.

In this article, we try to focus on what starting job roles can be ideal for someone looking for a data scientist’s profile. Data scientist role combines Statistics, technology and business acumen and all these 3 skills should work in tandem. These below listed profiles can be a starting point for someone who eventually would like to

Analytics India Magazine lists down six top job profiles that can make way to an ideal Data Scientist role

Data Modeler:  A data modeler is tasked with translating business needs into data models – schematic representation of data. The first step in database design, data modelers create a conceptual model to see how various data objects relate to each other. Data modelers come from a mathematical background and some of the most popular enterprise data modeling tools used are SAP PowerDesigner, CA Erwin and ER/Studio. Data modelers have a deep background in mathematics and statistics and have knowledge of popular programming languages. Besides, R and C++ are also part of their toolkit. An integral part of an agile project, their Entity Relationship Diagram (ERD) forms the basis of an enterprise’s data strategy. With a large experience of managing datasets, data modelers also have a good understanding of business requirements and the functional requirements of an application being developed. That’s why, they Data Modelers can go on to become Data Scientists.

R programmer: Between SAS and R, R emerged as a dominant choice of analytics tool among data analysts and this has led to a rise of R programmers. With R having more powerful data analysis tools, R programming is on a rise, especially in sectors such as BFSI and pharma where R is used more for simulation as opposed to analysis. In the changing skill landscape, R programmers are in high demand for their in-depth experience in automated report generation. And that’s why they make a perfect fit for a data scientist job.

Database engineer: Database engineer have long been propped up as the ideal candidate for the data scientist job. Here’s why- they manage the data pipeline and are tasked with the installation of database products, have in-depth knowledge of SQL, DBA scripts and other software. Some of the tools used by data engineers are Hadoop, Keras, Pandas and Python. Thanks, partly to the complexities of the job and their background in coding and managing data pipeline, database engineers usually transition to the role effectively.

Machine Learning specialist: ML specialists are all the rage these days, with deep expertise in running standard machine learning models ranging from regression, binary classification and multiclass classification. From training and optimizing ML algorithms to developing new applications, ML specialists are in huge demand in big and small companies for their heft in predictive analysis and fulfilling business requirements. ML specialists graduate to become data scientists and also go on to lead data science teams. Popular tools used are Python/R and Keras among others.

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Deep Learning Expert: A very rare field which requires great expertise, deep learning involves neural network, a subject of great research. ML specialists who have pursued neural networks and Convolutional Neural Networks in research go on to become Deep Learning experts. A relatively new profile, DL experts are drawn from the academic world and are staffed at leading tech companies across the globe – Facebook, Google, Baidu, Microsoft, OpenAI and not to forget DeepMind. DL guys work a lot on GPUs and work on problems such as image detection, and autonomous driving system. And that’s why they get out vote of acing the job of data scientist.

NLP specialist: A hard nut to crack in computer science, Natural Language Processing developers organize and structure data to perform tasks such speech recognition, sentiment analysis, text analysis, and more. Today, NLP specialists are in huge demand thanks to enterprises building chatbots for automated question answering and machine translation. Besides creating a chatbot, NLP experts are also tasked with sentiment analysis, Named Entity Recognition and summarizing blocks of text. They are staffed at marquee tech companies such as Facebook, Google, Amazon, ecommerce and financial sector. NLP algorithms are used to filter malicious comments, suggest trending topics and social media monitoring. NLP experts have deep expertise in machine learning algorithms, are exceptional coders, have hands-on experience in executing new data projects. Hence, they make a perfect data scientist.

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