In this article, we will continue our discussion from where we left off in part 2.
We were discussing about various roles in Data Science Domain. One can refer part 1 and Part 2 as we have discussed the roles till Deep Learning Engineer out of all the roles listed below.
Below is the list about the common job titles in Data Science Domain.
- Data Mining
- Data Analyst
- Big Data Analyst
- Business Analyst
- Data Scientist
- Data Engineer
- Big Data Engineer
- AI Engineer
- Machine Learning Engineer/Ml Ops Engineer
- Deep Learning Engineer
- Applied Scientist
- Research Scientist
- Robotics Engineer
- Product Manager
Now we will continue our discussion from Applied Scientist onwards.
- Applied Scientist
Applied scientist focuses on research whose results can be applied to solve their respective problem. These persons ask right question to work with a problem statement. They explore that question more in order to start research. Their research leads to some practical solution.
- Research Scientist
The research scientists conduct experiments and trials. They write research papers on their findings. They are also up to date with the newest developments in concerned field. An AI research scientist is one who is associated with AI field. Research Scientist majorly work in academics where they teach and perform research in their area of expertise. They can also work for some private or government firms where they conduct research in their domain of expertise.
- Robotics Engineer
Robots are automated machines which mimics human actions. Robotics engineer works in development of robots using AI technologies. A robotics engineer designs prototypes, builds and tests machines, and maintains the software that controls them. They also conduct research to find the most cost-efficient and safest process to manufacture their robotic systems.
- Product Manager
A product manager is the person who identifies the customer need and the larger business objectives that a product or feature will fulfill, articulates what success looks like for a product, and rallies a team to turn that vision into a reality.
Data Engineer vs Data Scientist vs ML Engineer
Data Scientist vs Data Analyst vs Machine Learning Engineer vs Machine Learning Researcher vs Software Engineer
