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What is The Difference Between Data Scientist and ML Engineer?

Data scientists and machine learning engineers are two important professionals in AI filed who play a vital role in model development. And their role in AI development is not that much different but from technical skills perspective there is difference.

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Data scientists and machine learning engineers are two important professionals in AI filed who play a vital role in model development. And their role in AI development is not that much different but from technical skills perspective there is difference.

Though, the core difference between data scientist and machine learning engineer is, former one more knowledgeable in programming skills used around data. While data scientist is is like mathematician who can program using his data analysis skills. However, their roles are complementary to each other and supportive.

ML ENGINEER VS DATA SCIENTIST

Actually, there are multiple parameters you can differentiate these two professionals. And if you are looking to hire machine learning engineer and shortlisting the data scientist you need to know the actual difference between these two AI specialists.

Educational Qualification Required for Data Scientist and ML Engineer

At the academic end, ML engineers both professional are graduated with highly qualified degrees and require decisive skills with extensive knowledge to perform their task in professional manner.

A ML engineer will typically more studious in computer science, while a data scientist is more involved in statistics or mathematics subjects. But let make you clear one thing, a ML engineer is programmer also specialized in data, while a data scientist plays with huge amount of data abut also a programmer.

At educational end, once you complete your undergraduate degree, you have to choose the right path and learn more knowledge and skills in that field.

Here, if you want to become a ML engineer you have options like either continue working as entry-level programmer or explore the opportunities into AI filed and become a specialist in bid data or machine learning programmer to develop an AI model.

Whereas, if you are ambitious to become a data scientist, you need to gain more education as master or doctorate degree to make your academic skills more strong and gain the capability to analyze and utilize the data for deep learning.

Technical Skills Required for ML Engineer and Data Scientist

Both engineers required extraordinary skills to work proficiently into their respective fields. Although, few of the skills are very common necessary for both of them to analyze the huge data and utilize its crucial information. Here, we brought the key differences between the skills of these professionals listed respectively.

Skills Needed For Machine Learning Engineer:

  • Computer Science Fundamentals
  • Strong ML Programming Skills
  • Proficient in Python/C++/R/Java
  • Probability and Statistics Modeling
  • Natural Language Processing
  • Understanding of ML Algorithms
  • Data Modeling and Evaluation Skills
  • Skills Required For Data Scientist:
  • Strong Statistical and Fundamentals
  • Big Data Analysis and Interpretation
  • Data-Driven Problem Solving
  • Machine Learning and Deep Learning
  • Data Visualization & Communication
  • Programming languages (R and Python)
  • Unstructured Data Management Techniques
  • Use big data tools like Hadoop, Hive and Pig

Roles and Responsibilities of ML Engineer vs Data Scientist

Both, a data scientist and machine learning engineer mainly hired to developed AI-enabled applications or autonomous models but they have different role and duties while working on such projects which are clearly outlined below.

Roles and Responsibilities of Data Scientist:

  • Data source identification and automated collection
  • Data Mining Using the State-Of-The-Art Methods
  • Enhance Data Collection Procedure and Techniques
  • Analyze Huge Big Data To Discover Trends And Patterns
  • Identify Trends, Patterns and Correlations in Complex Data Sets
  • Create Analytical Methods and Machine Learning Models
  • Assess the Effectiveness of Old or New Data Sources
  • Evaluate the accuracy of data gathering techniques
  • Apply and Implement the popular Deep Learning frameworks
  • Responsible to Undertake Processing of Unstructured Data
  • Use machine-learning algorithms to Build the Predict Models
  • Data Visualization, Presentation and Storytelling Techniques
  • Collaborate with ML Engineer and with other Stakeholders

Machine Learning Engineer Roles and Responsibilities:

  • Understand and Transform the Prototypes of Data Science
  • Research, Design and Frame Machine Learning Systems
  • Choose and Implement the Right Machine Learning Algorithm.
  • Select and Implement Right Machine Learning Algorithms.
  • Select the Right Training Data Sets for ML Model Development
  • Understand Business Objectives and Developing the Ml Models
  • Perform Machine Learning Model Tests and Experiments
  • Perform Statistical analysis and Fine-Tune the Testing Results
  • Verifying data quality, and/or ensuring it via data cleaning
  • Develop the Machine Learning Model as per the Needs.
  • Perform the Training models and tuning their hyperparameters.

The role and responsibilities of data scientists and machine learning engineer is more or less different but there are many roles they both play during their task. As they also need to work collaboratively to build a right AI model that can work with best level of accuracy when implemented in real life-use.

If you are looking to hire a machine learning engineer or data scientist, get in touch with right consultant to find the right candidate for your AI project. Cogito is providing the remote machine learning engineer hiring service for companies looking for such professionals to work on training data for AI or ML projects as per their customize needs.

Cogito offers a complete data collection and processing service for training data as a services for AI and Machine learning based services. It is providing wide range of services like Visual Search, Image Annotation, Machine Learning, Sentiment Analysis, Data Collection, Data Classification, Search Relevance, healthcare training data, contact center services, Content Moderation, Audio Transcription, Video Transcription and OCR Transcription services with high quality and accuracy.

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