Data science is a very vast field, and that is why we have many data science career opportunities to choose from. Each phase of data science is a huge task in itself and requires skilled people who have the necessary knowledge and experience.
There are many ways to get into data science. You could be, for example, a software engineer and take your career forward towards machine learning or data science.
Else, you could be a statistician wanting to learn more deeply about analytics and visualizations. You can also switch into different specializations within the various data science career opportunities.
What does Data Science Constitute?
Before we dive into the data science career opportunities, we shall briefly discuss what data science contains to appreciate each role well. Data science brings together 4 different fields – computer science, probability, statistics, and business/domain knowledge. All these require business intelligence and knowledge of big data.
Big data is a collection of humongous data sets that are so complex that relational database systems (RDBMS) are not able to handle them. To use this chunk of data, we need to perform data science on the dataset or its sample.
Data can be generated in many forms – raw or unstructured, structured, natural (human) language data, machine-generated data, audio, video, image, graph-based data, and streaming (real-time) data. Analyzing all these types of data requires the following steps:
- Setting the business/research goal
- Data collection
- Preparing data
- Data exploration
- Data modeling
- Visualization and automation
Check out the detailed process in our article on the Data Science lifecycle.
How Data Science is Shaping the Future?
From wearable devices to shopping preferences, movie recommendations, healthcare diagnosis based on patient history to booking cabs in real-time, logistics, and food supply management, data science has helped almost every domain productively and enhanced a company’s growth tremendously.
There are many data science career opportunities in every domain, and businesses can understand customer preferences through various channels and improvise their products and services accordingly.
Data Science Career Opportunities
1. Data Scientist
This is a complete role, where the data scientist has knowledge of all the phases of data science and works in one or more data science areas. For this role, knowledge of SQL, business domain, and statistics is essential.
Besides this, good communication and reporting/visualization skills help a data scientist present insights in a well-understood manner. All in all, a data scientist should be adept with technological, communication, and business skills. The salary offered to a data scientist is about $165,000-$250,000 per annum.
2. Data Analyst
The role of a data analyst is one of the most popular data science career opportunities. A data analyst deals with data collection and wrangling through various tools and techniques, data transformation, finding trends and patterns, and performing exploratory analysis before algorithms are applied to the data.
Data analysts work closely with machine learning engineers to provide insights and get more information from the data. Typically, the salary of a data analyst ranges anywhere between $107,000 to $117,000 per annum.
3. Data Architect
The role of a data architect is to create a blueprint for data management systems. Data architects analyze data from various sources to chalk out a plan to integrate, centralize, and protect data.
They implement proper security measures and give the right permissions to the right people for accessing critical data. Data architects should be adept in math, statistics, data visualization, cloud computing, and data migration. Additionally, they must have a good command over RDBMS and SQL. Data architects easily earn around $80,750-$141,250 per year.
4. Data Engineer
Data engineering is quite vast and includes all the aspects of data. A data engineer designs, builds and installs data systems that enable data analysis and processing. The data systems should be able to handle data transformation, modeling, visualization, etc.
A data engineer is one of the top jobs along with data scientists currently in the market. Data engineers are also called big data engineers as they handle the big data required for performing data science. A data engineer can typically earn about $120,000 per annum and work extensively with big data and related technologies like Hadoop and Spark.
5. Data Science Generalist
A data science generalist (who is not a specialist) can handle a host of multiple roles. Specialists are experts in a particular role, whereas generalists can be given any role at a particular time. A good team consists of generalists and specialists for efficient contingency management.
The main task of a data science generalist is to separate the good data from the bad data and provide high-level insights. Data science generalists should have good knowledge of big data frameworks, mathematics, statistics, and data analysis tools and techniques. A generalist usually gets around $70,000-$115,000 in a year.
A statistician works with applied or theoretical statistics, programming languages, like Java, Python, and R, big data frameworks, and database query language. The job of a statistician is to transform the raw data into more accurate, readable data and process humongous data sets using advanced tools.
Statisticians should be able to analyze data and present their insights to other team members like machine learning engineers and data analysts for further analysis.
These professionals are involved in various data science stages, like collecting data from various sources using methods like experimentation, surveys, interviews, polls, etc., or visualizing data in the form of graphs, charts, and reports. The typical salary of a statistician ranges from around $87,780-$113,670 per annum.
7. BI (Business Intelligence) Developer
BI developers should have excellent business acumen and communication skills other than domain knowledge. They are responsible for designing, developing, deploying, and maintaining business intelligence solutions.
BI developers should be able to fetch data by querying and present information to various channels by using visualization techniques. They should have data analysis and database knowledge and basic knowledge of tools and techniques for data science. The typical salary range for a BI developer is $80,150-$107,575 per year.
8. Machine Learning Engineer
A machine learning engineer performs data modeling, i.e., applies algorithms to the cleaned and transformed datasets to create a model that can be then generalized or scaled to production datasets. These models can then be applied to real-time data to determine trends and patterns in the data.
Machine learning engineers should be experienced with at least one programming language, like Python, R, and Java, and possess strong knowledge of data structures and algorithms.
ML engineers should be updated with the latest technological trends and be open to adopting new methods and approaches to solve a business problem. Machine learning engineers generally receive a good salary package anywhere between $125,000-$175,000 per annum.
9. Data Administrator
Although this is not directly a data science career opportunity, data administrators are now more in demand than before, after the explosion in the popularity of data science. Data administrators use tools and techniques to maintain the security and integrity of the database.
They ensure that the data is always consistent and helps in the development and planning of the database structure. If there are any issues with the database, the first point of contact is the data administrator, who has complete access to the database.
A data admin should be able to manage physical storage and design requirements, carry out capacity planning, and update the database design accordingly. They also give the required privileges and database access to other users. Data administrators get about $48,371-$79,000 per annum.
10. Data Analytics Manager
Data analytics managers oversee the overall progress of data analysis work and help the team solve any issues or obstacles. These professionals should have effective communication skills to collaborate with various teams to ensure smooth functioning of the entire data science process and possess good business and data analysis skills.
Data analytics managers should have excellent presentation, visualization, reporting, and critical thinking skills, in addition to the working knowledge of big data, machine learning, SQL, and programming languages. They get anywhere between $93,000-$174,253 per year.
Data Scientist Skills
If you search on the internet for the skills of a data scientist, you will be overwhelmed to see many! That is true as data science is a challenging and demanding job; you don’t have to be adept in all the skills at once though. Here is a list of important data science skills you will need to build your dream career opportunities in data science.
Data Scientist Demand
Those who never had a digital presence are slowly changing their strategies. With the demand for data further increasing, data scientists are much in demand and have a great future in the different career opportunities listed above. The demand will potentially increase in the next 5-10 years!
These are the core data science career opportunities. Some of them are specialized roles, like machine learning engineer and data analyst; some roles are more generalized, like data scientist or analytics manager.
Other than these roles, there are data architects, software engineers, and other technical people who can always build their career in data science easily as they already possess one or more skills required to be a data scientist. Learn how to become a data scientist.