ML Engineers vs. Data Scientists vs. Data Engineers: What's the Difference?
Introduction
Artificial Intelligence, Machine Learning (ML), and Data Science are becoming increasingly popular concepts nowadays. With data and information becoming abundant in the world as ever, the strategies to observe, process and analyze them are necessary. In order to cater for the needs of a changing world the job roles associated with a domain also increase.
The jobs which existed 10 years before in the IT industry are extinct. The same principle may apply to some jobs in the field today. Today let us broaden our understanding of three exciting career paths for all AI enthusiasts.
Data Scientists
Who are They?
Data Scientists use statistics and build statistical models to support a company’s business growth. They are specifically trained to analyze data and make predictions about the business.
Implementing solutions combining mathematics, statistics and computer science while conducting undirected research is a part of their job. Simply put, Data scientists are masters in identifying patterns to forecast the future based on past experiences.
Education Required
Typically, beginner Data Scientists have a bachelor’s degree in Computer Science or Data Science as the minimum qualification. Upon advancing in their careers many tend to achieve a higher education qualification such as a master’s or a PhD relevant to the same field.
Tools and Languages
Data scientists specialize in programming languages like Python and R.Also, they are fluent in database systems such as SQL as dealing with bulks of data is a vital part of their job.
As in many cases, they are expected to visualize and present their data, data scientists are fluent in using tools like Jupyter Notebooks, Python Scikit-Learn Library and modules like Pandas and Numpy.
Average Salary
According to Indeed the average base salary of a data scientist per year is $124 771. The salaries show a drastic increase along with the years of experience of the individual.
Data Engineers
Who are They?
Data Engineers are people who build, set up and maintain the required infrastructure to store large volumes of data and process them to meet business goals and objectives.
These infrastructures are used by data scientists to gather relevant information for their research and analysis. Data engineers are also responsible for transferring data back and forth between organizations and external entities.
Education Required?
Most data engineers will have a bachelor’s in Computer Science or Data Science but some entities do not make it a vital requirement. In fact, the large number of vendor-specific certifications thriving in the industry will provide you with the relevant skills required in achieving your dream. Oracle, Microsoft and IBM are a few companies which have renowned fame for providing necessary services.
Tools and Languages
Excelling in database management systems and pipeline tools such as SQL, Oracle, and Cassandra is a must when becoming a data engineer. For data analytics tasks excelling in Apache-Spark and Apache-Hadoop will be vitalKnowledge about streaming pipeline frameworks such as Apache-Kafka will also be effective in pursuing a career as a data engineer.
As data engineers are expected to work closely with Data Scientists and ML Engineers an understanding of Python and R programming languages will come in handy.
Average Salary
A beginner data engineer’s base salary is about $79 000 per year according to Indeed. After 8-12 years of experience, it will increase up to $110 000 per year.
Machine Learning Engineers (ML Engineers)
Who are They?
In simple words, ML Engineers deploy the models developed by the data scientists to the servers, so that everyone can benefit from them. ML Engineers typically design ML Systems, use appropriate datasets, research and utilize effective ML algorithms, run the developed models and debug/ re-train them to maximize their efficiency.
They also act as a bridge between data scientists and the real-world application of their findings. Unlike the other two roles, ML engineers have significant experience in computer science, statistics and also software development.
Education Required?
An advanced bachelor’s degree in Computer Science, Data Science, Mathematics or Statistics will set the entry point to your ML engineering career. In order to advance in your job you may have to pursue a master’s degree specializing in machine learning, deep learning or neural networks.
Experience in ML frameworks, libraries and packages is a vital necessity in this career. Most ML engineers start as Software Engineers and reach to their dream career “ML Engineer” upon their growing skills and experiences.
Tools and Languages
Knowledge of programming languages like Python and R will also be applicable to this role too. Other than that experience in languages like Java and C++ also expected to become an ML engineer.
The C++ language is recommended as some organizations utilize their ML engineers to supplement the development of embedded systems.
The most famous framework for ML engineers is Tensorflow. Common tools for model deployment in today’s industry are Microsoft Azure, AmazonSagemaker and Google Cloud ML.
Average Salary
According to Indeed the average base salary of a machine learning engineer is $150 844 per year. Based on the education and experience of an individual these salaries will show significant increments.
The Three Roles Working Together
Say we want to determine the estimated arrival time of a taxi in an online taxi services application. A Data Scientist will research and find what factors affect the arrival time of a taxi.
Based on those factors he needs to develop a model theoretically which will address the above problem. For this example let’s assume that factors that affect the arrival time are weather conditions and traffic conditions on the road. So, he contacts the data engineer to obtain the necessary data about those to continue his/ her research.
The data engineer conducts extensive research to obtain large numbers of data to address the Data scientist’s need. He/ She might contact local weather stations to get data about the weather history of the area and he/ she might reach out to transportation authorities to obtain data about traffic in an area. He arrange the data gathered eliminating unnecessary parts and supplementing the need of the Data Scientist.
Finally, the ML Engineer implements the Data Scientist’s model using computational frameworks and deploys it in the server so the community can benefit. Over time he might have to re-train the model based on newly gathered data. It should be noted that in a company in which all of these roles aren’t present one or more will overlap another.
