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What is the career path in Data Science?

Data Science is considered one of the most lucrative jobs in the industry right now. With numerous openings spanning across all sectors, data science jobs are showing only the signs of growth. As more and more companies are adopting data science, companies are hiring data scientists by hordes. However, despite India being a frontrunner in technical education and research, the demand-supply gap for data science jobs vs applicants is only widening. At this point of time in the analytics ecosystem, 70% of the job postings in this sector are for data scientists with less than five years of work experience. The career trajectory for a data scientist is slightly complicated to trace for different reasons. Most of the middle and senior-level management, with 10-15+ years of work experience, started off from software or coding designations since the sector wasn’t evolved enough to encompass the designation of a data scientist. However, things are changing now, and the succeeding generations of data scientists will have a more clear idea of their career paths. Here, we will address the ‘big four’ designations and their synonyms for data scientists (mentioned above) and trace their professional career path. 1. Data Scientist A ‘Data Scientist’ is the crème de la crème in any company. That is why this designation is most sought-after by professionals these days. Many organizations use this designation as it’s easy for aspirants to search for and apply. Other companies use designations like “Business intelligence expert” or “market analyst” for the same. Role: American mathematician and computer scientist DJ Patil defined the role of a data scientist as, “A unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data.” In the modern workplace, data scientists also have to build machine learning models for prediction, find patterns and trends in data, visualize data, and even pitch in with marketing strategies. Skillset: Statistics, Mathematics, Data Modelling, Python or R programming, Other skills: Database skills, Business acumen, Visualisation/BI, Presentation skills Corporate ladder: The corporate ladder for a data scientist/BI expert/ Market analyst would look something like the following. However, it is to be noted that organizations may rename some designations according to their convenience or in keeping with their corporate ladder structure. For example, a “lead data scientist” may be called “principal data scientist” in some organizations. 2. Data Analyst Organizations usually use this designation to communicate that this role involves more technical knowledge. Some of its synonyms are “Analytics Professional” or “Business Analyst”. Role: The role of a Data Analyst revolves around using the company data to generate actionable insights which then the C-suite can take action upon. Another interesting fact about data analysts is that their projects usually change from time to time. So for 3 months, a data analyst may be working with the marketing department, and the next, maybe shifted to production. Skillset: Data Modelling, Python or R programming, Tableau Other skills: Business acumen, Database cleaning skills, Visualisation/BI, Presentation skills Corporate Ladder: The corporate ladder for a Data Analyst/Analytics Professional/Business Analyst would look something like the following. However, it is to be noted that this designation also has the flexibility for lateral movement towards more specific and niche roles. 3. Data Engineer A Data Engineer is considered as the backbone of any big organization. Companies usually hire data engineers to channel their talents towards software development. Some of its synonymous roles are, “Data Architect” and “Quantitative Analyst”. Role: As a data engineer works with the organization’s core data infrastructure, this role requires a deep knowledge of programming skills. In most organizations, a data engineer is responsible for building data pipelines and correcting the data flow to make sure the information reaches the relevant departments. Skillset: Database management, data cleaning, Python or R programming, Hadoop Other skills: Business acumen, Database cleaning skills, Visualisation/BI, Presentation skills Corporate Ladder: The corporate ladder for a Data Engineer/Data Architect/Quantitative Analyst would look something like the following. As this role is more niche and central to the organization, lateral movement is uncommon. However, for the same reason, this job is the most impervious to layoffs. 4. Business Intelligence Developer A Business Intelligence Developer in any organization is considered as a sort of jack of all trades who basically has to have a firm grasp on the fundamentals of analytics as well as the IT department as a whole. Some of its synonymous roles include, “Systems Analyst” and “Machine Learning Engineer”. Role: A Computer Scientist’s role has a lot of overlaps with key functions including data science, programming and data architecture, among oth- ers. This role has greater impetus on technical, rather than analytical skills and requires advanced knowledge of all popular machine learning tech- niques. Skillset: Python or R programming, Hadoop, creating models, Notebook, Github, data modelling Other skills: Business acumen, Visualisation/BI Career Ladder: The corporate ladder for a Computer Engineer/Systems Analyst/Machine Learning Engineer would look something like the following. As this role has purview over almost all of the other departments, especially digital and emerging tech, there is also a great chance for lateral movement in the organization. Do visit: