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Data Science Careers
Data Scientist
A data scientist is a person who uses mathematical, statistical, and programming skills to gain insights from data. They will gather, organize, clean, and analyze data. This part is the same as with data analysts. However, they are more forward-looking and prediction-oriented. They will use the data to build the machine learning models. They help them make predictions by finding trends, patterns, and behaviors in the available data. They do it to solve business problems and increase the company’s performance in terms of sales, clients’ experience, costs, revenue, etc. Data scientists have to understand the challenges of business and offer the best solutions using data analysis and data processing. For instance, they are expected to perform predictive analysis and run a fine-toothed comb through an “unstructured/disorganized” data to offer actionable insights. They can also do this by identifying trends and patterns that can help the companies in making better decisions.

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Data Analyst
Data analysts are data workers who use data to describe past and present, while data scientists use it to predict the future. Data analysts are responsible for a variety of tasks including visualisation, munging, and processing of massive amounts of data. They also have to perform queries on the databases from time to time. One of the most important skills of a data analyst is optimization. This is because they have to create and modify algorithms that can be used to cull information from some of the biggest databases without corrupting the data. They are required to perform regular and ad-hoc analyses and provide reports. That way, they help make business decisions and unlock answers to some business problems. Data analysts are usually required to visualize data and communicate the results of their analyses.

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Data Engineer
Data engineers main task is to develop and maintain data infrastructure. Its purpose is to transform data into an “analyzable” format and make such data available to data scientists and data analysts. That means they have to gather, maintain, manipulate, and load data for others to use. Data engineers are more focused on extracting, transforming, and loading (ETL) data than data analysts and data scientists. Data engineers build and test scalable Big Data ecosystems for the businesses so that the data scientists can run their algorithms on the data systems that are stable and highly optimized. Data engineers also update the existing systems with newer or upgraded versions of the current technologies to improve the efficiency of the databases.

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Database Administrator
The job profile of a database administrator is pretty much self-explanatory- they are responsible for the proper functioning of all the databases of an enterprise and grant or revoke its services to the employees of the company depending on their requirements. They are also responsible for database backups and recoveries. This data science job title is in charge of, well, database administration. This means they work with data modelers and data architects in database implementation. Only they are more focused on practical and technical issues rather than conceptual. Their job is to ensure the availability of databases, which includes allowing (or not) access to databases, backup and restore data, ensuring data security and integrity, and high database performance.

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Data Architect
A data architect is a practitioner of data architecture, a data management discipline concerned with designing, creating, deploying and managing an organization's data architecture. A data architect creates the blueprints for data management so that the databases can be easily integrated, centralized, and protected with the best security measures. They also ensure that the data engineers have the best tools and systems to work with. Compared to data modeler and database administrator, the data architect is a data science job title that requires a high-level point of view. The data architect’s job is to have in mind the company’s business needs and develop the complete architecture of data management. This doesn’t involve just databases but laying out the framework for how the data will be collected, used, modeled, retrieved, secured. In general, this means providing an architecture that will be there from the point data enters the company to the point it leaves it. Data architects visualize and design an organization's enterprise data management framework, aligned with enterprise strategy and business architecture. Data architect is an evolving role and there is no industry-standard certification or training program for data architects. Typically, data architects learn on the job as data engineers, data scientists, or solutions architects and work their way to data architect with years of experience in data design, data management, and data storage work.

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Data Modeler
Data Modelers are Systems Analysts who design computer databases that translate complex business data into usable computer systems.Their job is to design, improve and maintain data models, which they then translate to database implementation. They do that with the purpose of improving data availability and database performance in general. To do that, they need to cooperate with data administrators and data architects. ... Their models are designed to improve efficiency and outputs, and may focus on issues such as reducing data redundancy or improving data movement across systems. The data modeler designs, implements, and documents data architecture and data modeling solutions, which include the use of relational, dimensional, and NoSQL databases. These solutions support enterprise information management, business intelligence, machine learning, data science, and other business interests.

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Machine Learning Engineer
Machine Learning Engineers are technically proficient programmers who research, build, and design self-running software to automate predictive models. An ML Engineer builds artificial intelligence (AI) systems that leverage huge data sets to generate and develop algorithms capable of learning and eventually making predictions. ML Engineer(Machine Learning Engineer) requires you to design, build, and maintain artificial intelligence (AI) software and algorithms that will automate predictive models and enable machines to function without being given instructions for every action. To do that, you’ll have to organize and analyze data that you will use for training and validating the machine learning model. This description shows the machine learning engineer is the same as a data scientist, except focused on both building and deploying the machine learning models. ML Engineers consider responsible AI throughout the ML development process, and collaborates closely with other job roles to ensure long-term success of models. They should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation, as well as familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance

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Business Intelligence Developer
The BI developer is a data-savvy engineer who develops and maintains BI interfaces and works in BI tools. Those are tools that allow querying and visualizing data, creating dashboards, regular and ad-hoc reports. In a way, this is a combination of a data engineer (ETL), data analyst (analysis & reporting), and software engineer (software development).

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Business Analyst
A Business Analyst assesses the company’s systems and processes. They analyze them and come up with solutions, usually in the shape of improved or new systems and other technical improvements. The purpose of this is to lower the costs and improve the company’s efficiency and decision-making, which should lead to earning more money. Business analysts work with organisations to help them improve their processes and systems. They conduct research and analysis in order to come up with solutions to business problems and help to introduce these systems to businesses and their clients.

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Statistician
A statistician, as the name suggests, has a sound understanding of statistical theories and data organization. Not only do they extract and offer valuable insights from the data clusters, but they also help create new methodologies for the engineers to apply.Their focus is only on the statistics part of the data scientist job. They, too, analyze data, apply statistical methods to data, and identify patterns and trends that will provide business insight and support decision-making.

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Selected Data Career Term

Data Engineer

Full Description

Data engineers main task is to develop and maintain data infrastructure. Its purpose is to transform data into an “analyzable” format and make such data available to data scientists and data analysts. That means they have to gather, maintain, manipulate, and load data for others to use. Data engineers are more focused on extracting, transforming, and loading (ETL) data than data analysts and data scientists. Data engineers build and test scalable Big Data ecosystems for the businesses so that the data scientists can run their algorithms on the data systems that are stable and highly optimized. Data engineers also update the existing systems with newer or upgraded versions of the current technologies to improve the efficiency of the databases.

Salary

$140000

Focus

Data infrastructure, Data warehousing solutions. Databases,Data cleaning, Data Preparation .Data Pipelines

Programming Lang

Python,R,Julia,SQL,Go,Scala,C/C++/C#

Roles

Design and maintain data management systems. Building Data Pipelines (ETL,ELT) Managing Data warehouses and Data Lakes Data collection/acquisition and management. Conducting primary and secondary research. Finding hidden patterns and forecasting trends using data. Collaborating with other teams to perceive organizational goals. Make reports and update stakeholders based on analytics. Big Data Management

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Data Science Careers

Data Engineer