In this article, we will continue our discussion from where we left off in part 1.
We were discussing about various roles in Data Science Domain. One can refer part 1 as we have discussed the roles till Data Scientist out of all the roles listed below.
- 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 Data Engineer onwards.
- Data Engineer
Data Engineers are the persons who are responsible to build and set a pipeline in order to gather large amount of different type of raw data and store it in a clean and predefined processed format so that it can be later used by business analysts, data analysts etc in order to draw some insights from data.
7. Big Data Engineer
A data collected in terabyte or petabyte is referred as big data. A big data engineer is responsible for setting up the pipeline in order to collect, clean, transform data and enriching it into different form so that the consumers like data analyst, business analyst and data scientists can extract the required information.
These guys generally work with data collection apis’ such as kafka, flink etc.
8. AI Engineer
An AI engineer develop AI solutions/products with the help of Machine Learning and Deep Learning algorithms. These guys have sound understanding of computer programming, software engineering, statistics, machine learning and deep learning. These guys can also take care of deployment. This vertical covers machine learning engineer and deep learning engineer.
9. Machine Learning Engineer/Ml Ops Engineer
Ml Ops which is also known as machine learning operations is inspired with Dev Ops. Dev Ops deals with setting up a CI/CD pipeline for software projects while ML Ops deals with setting up the CI/CD pipeline for Machine Learning projects.
ML Ops = ML + Dev + Ops
These persons take care of model optimization and their deployment. These guys are responsible to ensure that the complete model deployment pipeline works seamlessly.
This pipeline contains following steps.
- Model Optimization to make predictions faster
- Model Deployment
- Model Scoring
- Model Version Maintenance
- Data Version Maintenance
- Deciding when to retrain the model
10. Deep Learning Engineer
Deep Learning Engineers are responsible to implement state of art deep learning models which are developed by researchers in real life problems. These guys have a mix skill of engineering, deep learning and machine learning. These mix skills help them in performing model training and model deployment smoothly.
