When it comes to data science careers, the most prominent role is that of a data scientist. It is also, in some ways, the most misunderstood. So, let us start our discussion about other popular data science careers out there.

Popular Data Science Jobs

1. Data Scientist

A data scientist collects, cleans, and analyzes large amounts of structured and unstructured data to derive insights that organizations can use to make decisions. Companies hire data scientists for a variety of reasons. A data scientist, for example, might assist a pharmaceutical company in analyzing clinical trials to speed up research. They may be working on personalizing offerings for the customer in a retail organization. In a nutshell, they identify trends and use data to solve business problems.

• Data scientists are knowledgeable in applied mathematics, statistics, programming, and business. Although most jobs require a bachelor's or master's degree, it is not required. However, joining a data science certification course in Mumbai is a plus point since it offers practical training for professionals. 

• Data scientists make an average of $110,000 per year. This can easily exceed $200,000 with experience, not including bonuses and stock options.

2. Data Engineer

Data engineers use pipelines to prepare and transform data. Their job is to collect, process, and store organizational data so that data scientists and machine learning engineers can use it effectively.

• Data engineers are experts at cloud platforms, ETL (Extract, Transfer, Load) tools, programming languages such as Python, R, and SQL, data warehousing solutions, and so on.

• Data engineers can earn up to $100,000 on average. With experience and specialization, this can easily exceed $200,000.

3. Data Analyst

The data analyst, who identifies trends, patterns, and relationships within data and uses this insight to make business decisions, is, in many ways, a stepping stone to data science positions. They mine data, organize information, create visualizations, and present reports to stakeholders.

• While some jobs may require a bachelor's degree in mathematics or statistics, many roles today welcome degrees/qualifications in finance, economics, politics, and other fields. A data analyst's primary skills include database languages such as SQL, analytical tools such as Microsoft Excel, programming languages such as Python or R, computing environments such as MATLAB, and so on.

• Entry-level data analysts make between $40,000 and $60,000. Data analytics leaders with experience can earn more than $250,000. Data analysts can also learn additional skills in programming, machine learning, and other fields and seamlessly transition into those careers.

4. Data Architect

A data architect designs the systems and tools that data scientists, analysts, machine learning engineers, and artificial intelligence professionals use. They comprehend the requirements of data science professionals, design systems, implement new architectures, and stay ahead of regulatory compliance.

• The majority of entry-level data architects are students studying computer science, information technology, or a related field. A master's degree may help obtain senior-level positions. Data architects are knowledgeable about cloud technologies, data warehouses/lakes, systems analysis, and programming languages such as Java, Python, and SQL. Companies also expect them to be certified in areas such as the Certified Data Management Professional (CDMP), the TOGAF 9 Certification Program, and the IBM Certified Data Architect (Big Data).

5. Data Modeler

A data modeler converts real-world business requirements into data models. They collaborate closely with data architects to develop conceptual, logical, and physical data models. They assist application teams in the design of databases. In addition, for compliance, work with data governance teams.

• For these positions, a bachelor's degree in computer science or data science is typically required, as well as skills in SQL, cloud environments, relational and dimensional modeling, business domains, and the software development lifecycle. They have also worked with data modeling tools such as Erwin, ER Studio, MagicDraw, Oracle Designer, Visio, and others.

• A data modeler makes an average of $95,000 per year. With experience, this can rise to $150,000 or more.

6. Machine Learning Engineer

A machine learning (ML) engineer is a software developer who creates self-running software that uses data and automates predictive models. ML engineers bridge the data-software gap by developing programs that enable machines to function without direct human assistance. ML engineers are in high demand for a variety of roles. Twitter, for example, is looking for an ML engineer to identify trends and prevent misinformation.

• A machine learning engineer must have two sets of skills: 

1. Data science skills include data manipulation, querying data sets, developing and testing hypotheses, developing regression models, and so on. 

2. Ability to write algorithms in software engineering languages such as Python, Java, Scala, C++, and others.

So get started with your data science and ML career with the best data science course in Mumbai, accredited by IBM. Master the industry-relevant skills and get hired in MAANG companies.