Data Science is a huge field in which people are hyped to get a job in. However, if you have a look at the job postings, you’ll see a lot of varying terminologies like Data Scientist, Data Analyst, Data Engineer, and Machine Learning Engineer.
In this article, we’re going to explain these job roles in detail, going through the responsibilities, skill requirements, and average base pay for each role.
Data Analyst
Data Analysis involves acquiring data from sources like databases and data systems. After getting this data, patterns and trends are identified from complex datasets. Using these inferences, filtered and “clean” data is presented to stakeholders in the form of performance indicators and reports.
So, data analysts are responsible for analyzing the data in an organization. A data analyst collects, processes and performs statistical analyses on large datasets. They discover how insights from the data can be used to answer questions and solve problems.
Data Analysts interpret data, analyze results using statistical techniques and provide ongoing reports to the organization. They help in implementing strategies and process improvements that statistically improve the efficiency and quality of a process, and due to this, the organization itself.
The general skills & requirements for this job role are:
- Analytical skills to “see through” and draw conclusions on data.
- Communication skills to be able to work with Senior Analytics leaders and other business teams to find optimal solutions.
- Having a degree in a mathematics, statistics or computer science related field.
- Experience in languages used for data analysis like R, Python & SAS.
- Knowing how to work with databases like SQL, Hadoop, Spark.
Average base salary in the US: $62,453 / year
Average base salary in India: ₹523K / year
Data Scientist
Taking the definition of Data Science from our own article, it is the application of Mathematics and Statistics on real-world problems to solve them faster using computers. It involves Software Development, Machine Learning and Traditional Research to solve those problems which traditional algorithms will take too long to solve, or the problems they will solve inaccurately.
Being namesakes of the field, Data Scientist roles are the ones you’ll mostly be searching for and seeing on job boards. Data Scientists are the people who have the technical skills to solve complex problems, and have the curiosity to explore what problems need to be solved. Data Scientists work with product managers, engineers, and others to form a cross-functional team driving growth and increased efficiency of an organization.
They work on “Big Data”, mining and transforming it to bring operational insights to businesses. Generally, Data Scientists are expected to be the go-to data guys for the company. They may be responsible for reporting, analysis, and business intelligence. They are also expected to be database experts.
The general skills & requirements for this job role are:
- Ability to translate business objectives into actionable analyses.
- Ability to communicate findings clearly to both technical and non-technical stakeholders.
- Having a degree in a mathematics, statistics or computer science related field.
- Experience in languages used for data science like Python, R, Scala.
- Knowing how to work with databases like SQL, Hadoop, Spark.
Average base salary in the US: $113K / year
Average base salary in India: ₹1,050K / year
Data Engineer
Data Engineering, in short, is the ability to design, build, and maintain data warehouses. Data Engineering goes hand-in-hand with Data Science, because it produces the data sources which are ultimately used in data science.
Data Engineers are the professionals who prepare the data infrastructure to be analyzed by Data Scientists. They are software engineers who design, build and integrate data from various sources. Then, they write complex queries on it to make sure that it is easily accessible. Their goal is to optimize the organization’s data ecosystem.
They run ETL (Extract, Transform and Load) on big datasets and create big data warehouses which are used by data scientists for reporting or analysis . Since Data Engineers focus on the design and architecture of data, they are typically not expected to know any machine learning or data analytics.
The general skills & requirements for this job role are:
- Experience in building and maintaining ETL data pipelines.
- Ability to create and evolve data models & schema designs to improve accessibility of data for improved analytics.
- Having a degree in a mathematics, statistics or computer science related field.
- Experience with any of the cloud platforms.
- Experience in data scripting languages like Python & R.
- Knowing how to work with databases like SQL, Hadoop, Spark.
Average base salary in the US: $103K / year
Average base salary in India: ₹888K / year
Machine Learning Engineer
Similar to Data Science, we’ve talked about Machine Learning before, in our article here. The objective of Machine Learning (ML) is for machines to be able to learn from the data itself, without human intervention or assistance. ML algorithms learn to solve a problem by themselves using data, contrasting to a traditional program, where a programmer has to explicitly write a set of instructions.
The role of a Machine Learning Engineer is more specialized and well-defined in an organization than that of a Data Scientist. They are are responsible for designing, developing and deploying Machine Learning models at scale. Thus, they require the technical know-how of a Software Engineer and also the exploratory keen of a data scientist.
ML Engineers leverage various data sources to create robust machine learning models. These models, in deployment, are connected with the data pipelines created by the Data Engineers to ensure a smooth and efficient flow of predictions and data. ML Engineers are responsible for monitoring these systems in production by checking performance, ensuring prediction validation and prediction quality continuously.
The general skills & requirements for this job role are:
- Experience in exploring data sources and data, while selecting appropriate datasets and their representation methods.
- Ability to design machine learning systems, and oversee the platforms on which the solution would be deployed.
- Having a degree in a mathematics, statistics or computer science related field.
- Experience in ML languages like Python, Scala and libraries such as Scikit-learn, TensorFlow and PyTorch.
- Knowing how to work with databases like SQL, Hadoop, Spark.
Average base salary in the US: $114K / year
Average base salary in India: ₹794K / year
We hope that this article was useful for you to get the information that you needed to apply to these roles.
Subscribe to read more articles related to Data Science & AI! We will definitely be writing more about data science job roles, so if there is a job role that you think we missed or if you want an article focussed on preparing for a specific job role, be sure to reach out to us.