According to the World Economic Forum, the amount of data generated per day will reach 463 exabytes (1 exabyte = 109 gigabytes) globally by the year 2025. According to the Cybercrime Magazine, the global data storage is projected to be 200+ zettabytes (1 zettabyte = 1012 gigabytes) by 2025, including the data stored on the cloud, personal devices, and public and private IT infrastructures. Thus, almost every organization has access to large volumes of rich data and needs “experts” who can generate insights from this rich data. Of course, handling such huge amounts of data and using them to extract data-driven insights for any business is not an easy task; and this is where Data Science comes into the picture.
Data Science is a combination of several disciplines including Mathematics and Statistics, Data Analysis, Machine Learning, and Computer Science. Data Science is a huge umbrella with a plethora of roles available in the field such as a Data Scientist, Data Engineer, BI Developer, Data and Analytics Manager, etc. Almost all of these roles require to work on deciphering the business-related questions that need answering and in turn searching for the data related to finding these answers. You can execute this by learning data science with python and working on real projects. Along with business understanding, you also need to have analytical skills. These skills are essential to collect, clean, analyze, process and manage large amounts of data to find trends and patterns in the dataset. The dataset can be either structured or unstructured or both. The results of the analysis done are used to make strategic decisions in businesses.
In this article, we will look at some of the top Data Science job roles that are in demand in 2023.
Data Science Careers
Before looking at various job roles in Data Science, let us look at the three main areas of Data Science Careers. As Data Science is an intersection of fields like Mathematics and Statistics, Computer Science, and Business, every role would require some level of experience and skills in each of these areas. To build these necessary skills, a comprehensive course from a reputed source is a great place to start. You can look for data science certification courses online and choose one that matches your current skill levels, schedule, and the outcome you desire.
Mathematics and Statistics
Data Science job roles require the knowledge of Mathematics and Statistics because Data Science relies on Machine Learning algorithms which, in turn require knowledge of Mathematics to analyze and discover insights from data. Mathematical concepts like Statistics and Probability, Calculus, and Linear Algebra are vital in pursuing a career in Data Science. Some of the roles involving advanced analytics, Machine Learning, and Artificial Intelligence would require advanced knowledge of Mathematics and Statistics as compared to other business-focused Data Science roles.
Data science and coding go hand in hand. However, the level of coding required differs for different roles. Certain roles like Data Scientists require a good knowledge of coding compared to other roles. Data Science also requires applying Machine Learning algorithms, which is why some knowledge of programming languages like Python, SQL, R, Java, or C/C++ is also required. In addition, as Data Science involves handling and working with large volumes of data, knowledge of Data Structures and Algorithms helps in writing efficient and optimized code, which can significantly impact the performance of any Data Science application you are building.
Data Science is not just only about analyzing data, but it also involves making strategic business decisions to solve problems faced by the business, based on the findings of this analysis. For this decision-making process, you need to have an understanding of the industry, the problems faced by the business that need to be solved, and the impact of solving this problem. To make accurate conclusions based on the analysis of the data, you need to understand what that data represents in the first place. Without understanding the data and the business, it is difficult to build an accurate data analysis model, which in turn could hurt the business’s growth. Thus, to build a career in Data Science, you need to be familiar with how the business operates, its business model, strategies, problems, and challenges. Certain Data Science roles that are more business-focused, like Business Intelligence Developer, require people to have stronger business acumen as compared to other technology-focused roles like Machine Learning and Computer Vision Engineer.
Data Science Roles
As Data Science is a broad field, you will find multiple different roles with different responsibilities. You will find that the responsibilities of some of these roles overlap with other Data Science roles to some extent. Many companies often use these Data Science job titles interchangeably, thus, the responsibility involved in a particular job role also depends on the company under consideration. Let us now look at the top 16 roles in Data Science Jobs and their Job descriptions.
1. Business Analyst
Business Analysts conduct market research and analysis, focused on the product line and the overall profitability of the business. They identify business problems and opportunities to enhance the practices, processes, and systems within an organization. Using Big Data, they provide technical solutions and insights that can help achieve business goals. They transform data into easily understandable insights using predictive, prescriptive, and descriptive analysis.
Based on this analysis, they recommend changes and strategic decisions to optimize costs and improve internal and external reporting. They identify gaps in their existing processes and leverage available data for the growth of the business. This requires considering the potential impacts of possible solutions and implementing new systems. Business Analysts are expected to have business understanding, technical skills like Data Modeling, and Visualization tools like Tableau.
2. Data Analyst
Scientist. The work required in both these roles seems similar as both the job roles require finding data trends and patterns to make operational decisions. The responsibilities of Data Analysts are to acquire massive amounts of data, visualize, transform, manage and process the data, and prepare data for business communications. They typically work with structured data to prepare reports that can easily indicate the trends and insights and can be understood by users who are not experts in the field to inform data-driven decisions.
A Data Analyst’s job heavily requires skills like Python, SQL, and R as they also require querying the data stores to calculate key metrics of the business. They also need knowledge of Data Warehousing, Analytics, and Business Intelligence concepts, Data Visualization, etc. They perform A/B testing to analyze the output of the model and decide whether the model needs to be enhanced based on the testing results. Data Analysts require good knowledge of Mathematics and Statistics, Coding, and Machine Learning.
3. Business Intelligence (BI) Developer
A Business Intelligence Developer, also known as a BI developer, designs and develops strategies to assist business users to find the required information quickly and efficiently to make business decisions. BI Developers need to understand the fundamentals of business strategies as well as know the business model of the company in detail, as their work is business oriented. They are in charge of the development, deployment, and maintenance of BI interfaces. They simplify technical jargons and complex information into easy to understand language so that other people in the organization can understand and provide quantifiable solutions to complex problems. They use tools like Microsoft Power BI or Oracle BI to develop dashboards, reports, and Key Performance Indicator (KPI) scorecards.
They should know SQL queries, SQL Server Reporting Services (SSRS), and SQL Server Integration Services (SSIS) and a background in Data Mining and Data Warehouse Design. They also have an understanding of Database Management Systems, Online Analytical Processing (OLAP), and ETL frameworks as they are responsible for building OLAP using relational and multidimensional databases. Using SQL queries, they design, code, test, and aggregate the results to generate insights.
4. Big Data Engineer/Data Architect
With the growth of Big Data, the demand for Data Architects has also increased rapidly. Data Architects, or Big Data Engineers, ensure the data availability and quality for Data Scientists and Data Analysts. They are also responsible for improving the performance of data pipelines. Data Architects design, create and maintain database systems according to the business model requirements. In other words, they develop, maintain, and test Big Data solutions.
They use technologies like Storm or Spark, HDFS, MapReduce, Query Tools like Pig, Hive, and Impala, and NoSQL Databases like MongoDB, Cassandra, and HBase. They also make use of ETL tools, messaging systems like Kafka, and Big Data Tool kits such as SparkML and Mahout. To become a Big Data Engineer, knowledge of Algorithms and Distributed Computing is also desirable.
5. Business Intelligence Analyst
A Business Intelligence Analyst finds patterns and value in their company’s data, which is similar to a Data Analyst role at most companies. BI Analysts analyze data, work with SQL, and are comfortable with data visualization and modeling. They collect and extract data from warehouses using querying techniques, analyze this data and create summary reports of the company's current standings.
They suggest recommendations to management to increase the efficiency of the business and develop new analytical models to standardize data collection. The skills required to become a Business Intelligence Analyst are Database design, creation and maintenance, Data Mining and Analysis, Data Visualization tools like Tableau, Power BI, Data Security and Privacy, advanced SQL queries, ETL Framework, programming skills like Python, R, and familiarity with Cloud Technologies.
A Statistician has the responsibility of getting useful insights from data. They have a strong background in statistical theories, methodologies, and data organization, they work with all types of data and turn them into knowledge. They acquire, organize, present, analyze, and interpret data to reach valid conclusions and make correct decisions. To become a Statistician, one must possess statistics knowledge as well as domain knowledge.
They use statistical analysis tools to analyze data, identify patterns, trends and interpret the results using data visualization tools or reports. They maintain databases, statistical programs, ensure data quality, and devise new programs, models, and tools. Statisticians should be comfortable with R, SQL, MATLAB, Python, SAS, Pig, and Hive. Apart from these technologies, they should have expertise in statistical theories, machine learning, data mining, cloud and distributed computing tools, data visualization, and database management systems.
7. Data Scientist
Data Scientists are professionals who understand business challenges and aim to offer solutions to overcome them by employing data analysis and data processing of huge sets of structured or unstructured data. The primary responsibility of a Data Scientist is to provide actionable business insights based on their analysis of the data. These may be done by identifying patterns, anomalies, or trends in the data to predict the best decisions an organization can take to maintain sustainable, healthy growth in business and to make sound decisions backed by the usage trends of their products. In addition to business knowledge, they must possess technology and social science skills in addition to industry-specific knowledge.
A modern-day Data Scientist performs the above in collaboration with engineering, business, and product teams to seamlessly integrate data-driven decisions in their processes. To become a data scientist, one would need a good understanding of R, MATLAB, SQL, Python, and other complementary technologies. It is also good to develop presentation and communication skills to help others understand one’s findings and their implications on different areas of the company.
8. Computer Vision (CV) Engineer
A Computer Vision (CV) Engineer is a specialized role that involves applying Computer Vision, Deep Learning, Machine Learning algorithms to give computers the ability to perceive information from images or videos. A CV Engineer uses software to handle the analysis and processing of large image datasets to automate the visual perception process, i.e automate the extraction, analysis, and understanding of useful information from images. CV Engineers work on applications like Image Recognition and Segmentation, Object Detection, 3D Scene Reconstruction, Scene Understanding, Active Perception, etc.
They are responsible for building and deploying Deep Learning architectures for which they should know Computer Vision Frameworks and libraries like OpenCV, and Deep Learning toolkits like TensorFlow, PyTorch, Keras, etc. As Computer Vision is used to analyze images, knowledge of image and signal processing is a must. One should also have familiarity with any programming language like Python or C++.
9. Natural Language Processing (NLP) Engineer
Similar to a Computer Vision Engineer, a Natural Language Processing (NLP) engineer is also a specialized role involving applying Machine Learning concepts to give computers the ability to interpret textual information. Natural Language Processing Engineers develop applications that can understand natural language data, i.e., human languages.
NLP Engineers require excellent skills in statistical analysis, text representation, and experience with Machine Learning and Deep Learning frameworks and libraries. Apart from proficiency in a programming language like Python, Java, or R, NLP Engineers also need expertise in text representation techniques like Bag of Words, N-Grams, Semantic Extraction, and Modeling.
10. MLOps Engineer
Machine Learning Operations, also known as MLOps is the collaborative link between the Data Science team and the Operations or Production team. This link is designed to automate the processes as much as possible to produce richer and more consistent insights. MLOps aims to bring systemization in Machine Learning Development in organizations.
MLOps Engineers automate the training and evaluation processes for machine learning models, feature engineering, and ensure that the data for model training is cleaned and readily available. They should have experience with MLOps tools and frameworks, Kubernetes, AWS Sagemaker, Kubeflow, Google AI Platform, Azure Machine Learning, etc. Proficiency and experience in Python, Unix, CI/CD pipelines are also a must.
11. Data Engineers
Data engineers are IT professionals whose responsibility is the preparation of data for operational or analytical use cases. They work on constructing pipelines and funnels of data to accumulate information from various sources. They clean, cumulate, connect and structure data for analysis-based applications. This type of engineer aims to facilitate the ease of access to data as well as the improvement and maintenance of their company’s big data system. Once you become a data engineer, you work on different sizes of data based on the size of the organization they work for.
The purpose of data engineering is to work in tandem with data science teams to better the visibility of data to ensure that companies can make sound business decisions. Data scientists work on deploying algorithms to the prepared data by the data engineers. The main skills required to be a Data Engineer include a working understanding of data warehousing, Python, R and SQL, ETL tools, machine learning, NoSQL and Apache Spark Systems, and relational DBMS.
12. Machine Learning Engineer
Machine Learning is another role that has a very high demand today. Machine Learning Engineers need to be familiar and work with various machine learning algorithms like prediction, classification, clustering, and anomaly detection to tackle business challenges. Strong statistics and programming skills are instrumental to becoming an ML Engineer. In addition to designing and building machine learning systems, machine learning engineers need to perform A/B tests, build data pipelines while monitoring the different systems’ performance and functionality.
They need deep expertise in technologies like SQL, Python, Scala, Java, or C++. They collaborate with other teams to improve the data quality and also monitor the performance to ensure the machine learning systems are reliable. They should have familiarity with building highly scalable and distributed systems as they deal with huge datasets. They need a strong mathematical and statistical foundation. This role is much more technical as compared to the other Data Science roles.
13. Data and Analytics Manager
A Data Analytics Manager oversees the work of the analytics department and provides the direction for the team of Data Analysts and makes sure the right priorities are set. To become a Data and Analytics Manager, strong technical skills in technologies like SQL, R, Python, SAS are needed along with the business and social skills to manage the team.
They are also involved in hiring decisions and deciding where each analyst’s skills will prove most productive for the organization. They lead the process development for effective data analysis and reporting. They possess the ability to produce understandable and actionable reports.
14. Data Storyteller
Data storytelling means communicating information with a compelling narrative. In simple words, Data Storytelling involves sharing stories as a means of sharing information. Data Storytellers visualize data, make reports and they also find the narratives that can describe the data in the best way and develop creative ways to express that narrative. Data Storytelling is somewhat a creative role that comes in between data analysis and human-centered communication.
They simplify the data to focus on a specific aspect, analyze the behavior and create a story that helps other people to understand a phenomenon in a better way. Data Storytellers possess Data Visualization skills, design thinking, knowledge of BI and design tools, and various soft skills such as creativity, communication, and the ability to craft a meaningful narrative.
15. Machine Learning Scientist
While the other Data Science roles are more focused on using the data to generate and present insights, a Machine Learning Scientist rather works on developing the algorithms that can be applied for these Data Science use cases. Machine Learning Scientists are often part of the Research and Development (R&D) Department, and they develop and design new Machine Learning algorithms and approaches.
Their work usually results in research papers that are published. They mostly work in academia; however, it is not unlikely to find Machine Learning Scientists in the research industry. To become a Machine Learning Scientist, an advanced degree specializing in Machine Learning or other similar fields becomes extremely important.
16. Database Administrator
Database Administrators manage the databases in organizations. They are responsible for continuous monitoring of the database to ensure proper functioning, security of data, management of users’ access and permissions to databases and they ensure availability of data by taking frequent backups and recovering data if required. They work on database software to store, organize and manage data and also help in the design and development of databases.
They are also responsible for testing databases to ensure reliable operation. Database Administrators require excellent knowledge of SQL and Scripting. They should have data backup, recovery, security, database design, and development skills and they should be proficient in at least one of the database management systems like Microsoft SQL Server, IBM DB2, Oracle, or MySQL.
Opportunities in the Land of Data
Data is one of the most powerful tools in the world today for almost anything we do. It is a strong and true indicator and reason to make decisions and is employed by every business, company, or individual in every aspect of their lives. Data is here to stay and hence the roles described above that relate to its study or inferences are also here to stay and grow. Take the Knowledgehut Data Science Training in Python and learn skills that will keep you relevant with the growing demand.
The responsibilities of each role may overlap with each other a bit, but these roles are the fundamental building blocks in the chain of usage of data to grow businesses exponentially. The aim of this prose has been to elucidate their intricacies a little better to help you make an informed decision towards your dream career.