A machine learning engineer isn’t expected to understand the predictive models and their underlying mathematics the way a data scientist is. A machine learning engineer is, however, expected to master the software tools that make these models usable.
With the rapid development of Artificial Intelligence, there are newer jobs daily coming up in the market. And there is some misunderstanding about the positions of machine learning engineers vs data scientist, mainly since both are comparatively new emerging fields.
When you sort it out and explore the terminology, the details, differences & roles should become apparent.
We are considering at a professional level about scientists and engineers roles. While a scientist needs to better understand the science underlying principle behind their research, an engineer’s job is to just build something.
Machine learning engineers and data scientists are two of the trendiest Jobs in the market right now. With 2.5 Quintillion bytes of data being created every day, the expert who can manage this massive data to produce desirable solution is in total demand.
Competition is rising between machine learning engineer vs data scientist and the gap between them is decreasing.
In this article, we will start by explaining what each of the profile means and then compare both of them on professional fronts. It will then be followed by a machine learning engineer VS data scientist comparison.
Who is a Data Scientist?
A data scientist is a specialist who gathers, analyzes, and interprets extremely large quantities of data. The position of a data scientist is an extension of many conventional technical positions including a mathematician, scientist, statistician, and qualified computer scientist.
What do they do?
Most of the companies look out for data scientists for gathering, processing, and deriving valuable insights from the data when a company needs to address questions or fix a problem.
When a company hires data scientists they will start exploring all areas of the business and devise strategies using programming languages such as Java to conduct robust analysis.
Along with many other techniques, they will use digital experimentation to help companies grow and prosper.
In addition, they can create customized data solutions to make businesses better understand themselves and their customers to make critical business decisions.
Who is a Machine Learning Engineer?
Machine learning engineers upload data into models generated by the data scientists. They are also responsible for carrying theoretical models of data science and helping to extend them to production-level applications that can accommodate terabytes of real-time data.
What do they do?
Machine learning engineers are working at a crossroads between software engineering and data science. They harness big data tools and programming frameworks to reinvent the raw data obtained from data pipelines as data science models prepared to expand as required.
Machine learning engineers often create systems that control machines and robots. The algorithms built by machine learning professionals allow a computer to recognize trends in its own programming data and educate itself to comprehend instructions and even think for itself.
Machine Learning Engineer VS Data Scientist
A data scientist’s position these days has become much more generalized and broad-based to the degree that it could fully supersede Machine Learning. And yet, there are cases where a data scientist does not perform data analysis on the data itself.
A data scientist’s roles can be multifarious. Manipulating, processing, and querying large volumes of concurrent data is now an incredibly skilled task in the age of ‘Big Data’ technologies. Thus, a data scientist’s main function may be, for example, to run and manage an architecture to absorb a wide range of data from various sources.
Statistics differ greatly in terms of wages and future growth! But one thing is fore certain that t he number of positions for data scientists well outruns the number of jobs for engineers in machine learning.
And it can be said confidently that these jobs will certainly not go anywhere, at least for the next 20 years, as the amount of data and its complexities will continue to increase significantly more.
Have a look at the following machine learning vs data scientist comparison.
A lot of Data Scientists’ job posting appeared and was flooding the market in the previous years. The same for the Machine Learning Engineer position is happening, it’s a fairly new one and is slowly evolving in areas where we have data specialists.
Now, if we compare Machine Learning Engineer vs Data Scientist, we need to consider a couple of parameters:
- Programming languages
These are some of the factors that will tell you a lot about both of the fields, namely machine learning and data science.
|Machine Learning Engineer||Data Scientist|
|Average US Salary||$114k-$150k||$113k-$154k|
|Skills||> Language, Audio and Video Processing|
> Applied Mathematics
> Signal Processing Techniques
> Software Development
|> Creative and Critical Thinking|
> Effective Communication
> Statistics & probability
> Data visualization tools (Power BI or Tableau)
|Programming Languages||Java, C++, or Scala and Python||SQL, Python or R|
|Experience||Linux based systems, |
Data structures and algorithms, profiling
|AWS/Spark, git/GitHub, signal processing, sensor data, time series, spatial data|
Apart from the above requirements, following qualifications are also required from these professionals:
Machine Learning Engineer
Below are the qualifications that are expected from a Machine Learning Engineer:
- Master’s or Doctorate in computer science, mathematics, or statistic
- Knowledge of vision processing, deep neural networks, Gaussian processes, and reinforcement learning.
- Strong analytical skills
- Experience using programming tools like MATLAB.
- Knowledge of distributed systems tools like Etcd, zookeeper, and consul.
- Knowledge of messaging tools like Kafka, RabbitMQ, and ZeroMQ.
- Understanding of machine learning evaluation metrics and best practices.
Below are the qualifications that are expected from a Data Scientist-
- Master’s or Doctrate in computer science, engineering, mathematics, or statistics
- Strong mathematical skills.
- Experience in statistical and data mining techniques (generalized linear models/regression, random forests, trees, and social network analysis).
- Understanding of advanced statistical methods and concepts.
- Knowledge of machine learning techniques such as artificial neural networks, clustering, and decision tree learning.
- Knowledge of web services like DigitalOcean, Redshift, S3, and Spark
- Knowledge of computing tools like Hadoop, Hive, Gurobi, Map/Reduce, MySQL, Spark, Business Objects, D3, ggplot, and Periscope.
Roles & Responsibilities of Machine Learning Engineer
Following are job roles and responsibilties of a Machine Learning Engineer:
- Understanding company goals and designing models to better accomplish them, and measurements to monitor their development.
- Manage the resources available, such as hardware, data, and personnel to meet deadlines.
- Analyze the ML algorithms that can be used to solve the problem statement and rank them according to their probability of success.
- Exploring and analyzing information to develop a better understanding of it and then finding variations in the distribution of data that could impact output when implementing the model in the modern environment.
- Verifying and/or ensuring data quality through data cleansing.
- Monitoring the process of data acquisition where more data is required.
- Attempting to find available online datasets that can be used.
Roles & Responsibilities of Data Scientist
Following are Job roles and Responsibilties of a Data Scientist:
- Consult with stakeholders to develop how company data can be used for useful business solutions.
- Look for ways to get new sources of data and test their reliability.
- Peruse and evaluate corporate resources to make product creation, marketing strategies, and business processes easier and better.
- Build custom models and algorithms for the data.
- Using predictive models to boost customer service, targeting advertising, increasing revenues, and more.
- Create the evaluation model quality of the organization, and the A/B test system.
- Coordinate the implementation of models with growing technical/functional teams and track outcomes.
- Create procedures, techniques, and tools to evaluate and track the performance of the model while maintaining the accuracy of data.
Top Companies using Machine Learning & Data Science
The following are some of the sectors where machine learning and data science have been in use–
|Application||Data Science||Machine Learning|
|Healthcare||Amara Health Analytics||Catalyst, Healthcare.ai, etc.|
|Banking & Finance||Citi Bank, Bank of America||US Bank, Citi Bank, Bank of NY|
|Automobile||Tesla, drive.ai||Tesla, AutoX, Optimus Ride|
To Sum Up
When you take a moment and look at all of these professions, you can see that machine learning engineer vs. data scientist jobs is not just a thing to debate. Rather, it’s more about what you are interested in working with and where you’ll see yourself from now on for several years.
Whether you are a machine learning engineer or a data scientist, you will work at the cutting-edge of technology and business. And as demand for leading-tech talent greatly outstrips supply, the rivalry in this area for brilliant minds will continue to be increased for generations to follow.
And no matter which direction you chose, you just can’t really go wrong.
If you or anyone in your company is looking out for a professional machine learning engineer or a data scientist, then we recommend you to have a look at Codersera’s website once. We offer you the ability to hire a highly experienced professionals.
Feel free to drop by and ask questions in the comment section below!
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