Data Science Jobs Roles and Responsibilities


Data Science Jobs Roles and Responsibilities

In an industry where new jobs are continually being created, data science has shifted from a buzzword into a strategic component of the business world. Also, data scientists are playing an increasingly strategic role as companies use a product-centric view of data. This area promises tremendous employment growth and higher earnings potential. There is significant job growth in this sector. Data analysis experts are currently benefiting from the wave of big data.

This exponential growth of the sector has given rise to a new trend: jobs are becoming more formal, admission requirements are strict, and new titles are emerging. For example, we see Business Analyst and Business Intelligence Analyst jobs that do not seem so far apart, but professionals have a different career path and revenue potential.

With the increase in jobs, new job titles go hand in hand. Some of these responsibilities may overlap, but there are also apparent differences. Today, Data Science offers non-MINT majors that do not have the programming context to enter the market.

7 Fastest Growing Job Roles In Data Science

1) Data Scientist

Role: As the world’s most prominent post-millennium job, the demand for data scientists is widespread, from large corporations to start-ups and majors of e-commerce. The market gradually evolves to become a computer specialist in its own right, working on a specific application such as NLP or Computer Vision and developing data-oriented products at the forefront of the market. With the growing need for talent in space, more data mining courses, programming, analysis, etc. are proposed.

Essential Skills: Experience with machine learning algorithms/algorithms in predictive modeling and analysis; Analysis techniques such as regression, classification, clustering, and time series are preferred for Data Scientist roles. Lately, Python has proven to be the most researched skill by scientists.

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3) Data engineer

Role: Data engineers have become a central part of the data science teams and one of the most challenging positions to fill. It seems that companies are struggling to find data engineers with the right experience and expertise. Data Engineers are responsible for building data pipelines and supporting pilot case use cases in the production phase.

Essential Skills: Data engineers have experience with Hadoop-based technologies such as MapReduce, Hive, MongoDB, or Cassandra. You also have in-depth knowledge of data warehousing and NoSQL technologies. Big Data technologies should master general programming languages ​​and higher programming languages, such as Python, R, SQL, and Scala.

3) Business Analyst / Data Analyst / BI Analyst

Role: Business Analyst understands business and data needs, translates operational needs of customers, has extensive domain expertise, and can also collect data and generate reports. The critical competencies required are the combination of analytical and operational capabilities to move projects forward and a deep understanding of how business analytics can add value to businesses.

The main difference between Business Analyst and Data Analyst is that DAs work with large amounts of data has a quantitative basis, programming, and analytics experience, while BAs work more commercially, collecting and analyzing data, including scope and project requirements, making effective decisions. Both roles require different skills.

Meanwhile, a BI analyst works primarily on reports, creates and implements reports and dashboards, and makes recommendations based on results. BI analysts have excellent dashboard skills and are proficient in SQL and Tableau. However, if you are geared towards compensation and want to plan a new career, a course will allow you to develop essential skills for all roles that use a lot of data.

Essential Skills: Excel, SQL, Tableau to advanced tools such as R, Python Data Science

4) Business Analysis Manager

Role: As one of the most popular and sought-after parts, BA managers are in great demand for managing analytics solutions, collaborate closely with the technical teams responsible for implementing these solutions, and expertise and, most importantly, strong consulting skills.

It is interesting to note that this sectoral expertise sought can also contribute to changing the career ladder to a data scientist’s role.

Essential Skills: Experience with R, Python, SQL, Tableau, Excel VBA, SAS

5) Big Data Specialist

Role: Big data specialists are at the forefront of solving the most complex data problems, especially in organizations with a high volume of data. Businesses need Big Data specialists experienced in database and analytics technologies, working in an enterprise environment, and managing global storage and data analysis projects. This role also bears the title of Big Data Developer or Database Engineer.

Essential Skills: Experience with Big Data technologies such as Hadoop, Spark, Pig, and Hive, and knowledge of OLAP and reporting. Understanding statistics and machine learning is an added value—experience in statistics and machine learning.

6) Machine Learning Engineer

Role: Machine learning engineers are the backbone of customer-facing technology companies that work with large amounts of data. ML engineers need to design the solution architecture for applications and automate the model training, review, and deployment process to ensure continuous deployment. In short, ML engineers provide that machine learning models and pipelines come into production.

Essential Skills: ML’s engineers have in-depth knowledge of data structures, algorithms, and object-oriented programming. They also have a background in classical and modern machine learning techniques such as decision trees, classification, regression, and neural networks. His core competencies include structured and unstructured data mining and feature engineering.

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7) MIS Consultant / Data Visualization Consultant

Role: MIS consultants are responsible for developing and managing a data collection, reporting, and dashboard platform. Data visualization experts collect data, analyze it, and work on reports and dashboards. Their job is to run and design reports and dashboards. They have extensive experience with various BI and SQL tools.

Essential Skills: Data mining, reporting, writing SQL queries and dashboards, and working with team members are crucial skills.