People are always saying that Data Science is so hot and expected highly paid in the industry. There are lots of people trying to join in the industry. Everyone would like to become a data scientist. However, there are not many candidates really fit for the role as a data scientist.
In this article, I would like to share different job roles in the data science industry. If you are interested in the industry, you may find yourself the most suitable role aligned with your ability and interest. Personally, there is no such easy job in the data science world.
(Figure 1: it is expected to have a Data Science team rather than a superman)
There are a number of roles being listed with their required skills:
- Data Analyst
- Data Engineer
- Database Administrator / Big Data Administrator
- Data Architect
- Business Analyst
- Data Scientist
- Statistician
- AI / ML Engineer
- Data Manager
Data Analyst
Data analysts are responsible for a variety of analyzing and reporting tasks including isualization, munging, and processing of massive amounts of data for summarizing a picture like a report / dashboard. 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. The top managers are seeking to have the report / dashboard immediately and new requests by ad-hoc requests. It is quite challenging for pulling huge volume of information from some of the biggest databases (data warehouse) or big data repositories.
Required skills for data analyst:
It is required to have skills like BI Tools (IBM Cognos, Tabelau, Qilk, etc.) and some basic programming on SQL, R, SAS and Python for data analysis. Some analysts may use Excel to solve most of the problems with Macro VBA and formulas.
Data Engineer
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.
Required skills for data engineer
If you are interested in a career as a data engineer, then technologies that require hands-on experience include Hive, NoSQL, R, Ruby, Java, C++, and Matlab. It would also help if you can work with popular data APIs and ETL tools, etc.
Database Administrator/ Big Data Administrator
They are responsible for the proper functioning of all the databases or Hadoop depending on their organization data architecture based on business needs. Moreover, they are authorized to grant or revoke user access privileges on different types of data according to the users’ job natures. They are also responsible for database backups and recoveries.
Required skills for data / big data administrator
A database administrator should handle database backup and recovery, data security, data modelling, and design, etc. Also, it is better for a database administrator to be good at disaster management and with deep knowledge in infrastructure like storage technology, operating systems, dockers and networking, etc.
Data Architect
A data architect is a person with board knowledge in data technology and information technology. He is key in designing the overall solution as the blueprint for the data management so that the data lake (or data warehouse / big data) is scalable, reliable and time-proven solution. It is expected the design can be centrally managed, easily integrated, expanded with business growth or environment changes, and well protected with the best security standard not limited to personal data. However, he is also able to design the blueprint under the provided budget.
Required skills for data architect
For this role, it requires expertise in data warehousing, data modelling, extraction transformation and loan (ETL), etc. If the organization is equipped with Big Data solution, the data architect should experience in Hadoop, Hive, Spark, and Spark, etc.
Data scientists
Data scientists are responsible to build the data analytics solution based on the business challenges. As a data scientist is a key person for an analytic team, he may work on the areas below:
- Pulling data
- Merging data
- Analyzing data
- Looking for patterns or trends
- Using a wide variety of tools, including R, Tableau, Python, Matlab, Hive, Impala, PySpark, Excel, Hadoop, SQL and/or SAS
- Developing and testing new algorithms
- Trying to simplify data problems
- Developing predictive models
- Building data visualizations
- Writing up results to share with others
- Pulling together proofs of concepts
Some of the tasks above may be shared with data engineers. They are always trying to convert predictive analysis result into actionable insights for the decision maker(s). A data scientist should not only a technical professional but also with very good industry domain knowledge for the data being handled.
Required skills for data scientist:
A data scientist should be very strong in R, MatLab, Python, SPSS or SAS Data Miner, etc. It is expected to have comparative advantages for a data scientist with a Master degree in Mathematics or Statistics or Information Systems, etc.
AI (Artificial Intelligence) / ML (Machine Learning) Engineer
This role is quite popular and in high demand recently. Everyone and every company is willing to implement AI projects or tasks with AI. Machine Learning engineers should be familiar in SQL, REST APIs, TensorFlow or other AI framework, etc. They are also expected to perform A/B testing, build data pipelines and implement common algorithms such as classification, clustering, regression, etc.
Required skills for AI / ML Engineer:
It is expected an AI/ML engineer to be a person with extremely strong technical skills on TensorFlow or other AI framework and programming ability on R, Python, etc. Meanwhile, this person should also be very strong in Mathematics and Statistics – which is very similar to a data scientist.
Statistician
A statistician who is responsible for the statistical theories and extracting valuable insights with a deeper manipulation with different statistical model. One of the key differences between a data scientist is that a statistician will work out the details like pushing the predictive model accuracies from 95.6% to 98.7%. A data scientist is working with statistical model but will not go into the depth on the statistical theories. Again, this is an optional role as not many company could affect the dedicated statistician.
Required skills for statistician
A statistician should have a bachelor or master degree in statistics. They are also responsible for creating new methodologies or picking the best-fit statistical model for engineers or data scientists to apply.
Business Analyst
A business analyst should not be a person technical focused. However, it is vital to highlight that this role is optional if your data team with good knowledge in the business operation and/or data source(s). He is an individual for acting the communication channel between the technical data science people with business users and managers.
Required skills for business analyst
They should have an understanding of business operations for different departments and business intelligence, and better to have knowledge on the IT technologies like data modelling, data visualization tools, etc.
Data Manager
It is not a good idea for this role being taken a person with just Information Technology background nor with only business management background. A data and analytics manager oversees the data science operations and assigns the duties to their team according to skills and expertise. They should be experienced in technologies like SAS, R, SQL, etc. and people and/or project management.
Required skills for data manager
For people in the technical field like data science, many data engineers and data scientists are very weak in communicating with people from other background. So, it is very important to have a manager with excellent social skills and leadership to manage the team of experts. Moreover, this manager is likely the most open-minded and innovative to lead the team for solving new problems.
At this point, you may ask “what is the team structure of your company?” My company is a small consulting company and it is in a flat structure with basically job title as data engineers and data scientists. Some of the roles are being shared like architecture and Machine Learning.
My Final Words:
Many data analysts and data scientists are focused on data and/or technology without the understanding of work-flow and data-flow.
Data is just the presentation of human behaviour. If your focus is just data (numbers only), I am afraid your analytic result is never reaching insights. If you put technology in the 1st priority, I think you would only great on a tool but never solving a problem.
I would like to suggest:
- Please spend more time to understand the meaning of data by #research.