Machine Learning Engineer vs. Data Scientist

Last updated: February 8, 2026Reviewed date: February 8, 2026Reading time: 7 min read
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Article Summary

Machine learning and data science are the keys to modern business. Nearly every industry is being disrupted by these two technologies, which are growing at a rapid pace and changing the way we live. They're already so prevalent that many of us don't even realize how much they've changed our world—or what it's like to live without them. What is a Machine Learning Engineer? A...

Key Takeaways

  • This article explains What is a Machine Learning Engineer? in simple medical language.
  • This article explains What is a Data Scientist? in simple medical language.
  • This article explains Machine Learning vs. Data Science in simple medical language.
  • This article explains Roles and Responsibilities of ML Engineer vs. Roles and Responsibilities of a Data Scientist. in simple medical language.
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Definition

Machine learning and data science are the keys to modern business.

Nearly every industry is being disrupted by these two technologies, which are growing at a rapid pace and changing the way we live. They’re already so prevalent that many of us don’t even realize how much they’ve changed our world—or what it’s like to live without them.

What is a Machine Learning Engineer?

A machine learning engineer is a person who develops, optimizes, and maintains algorithms that they can train to solve problems based on data.

Machine learning engineers use large amounts of data to build models that can predict future events or outcomes. They typically work in teams with other data scientists and software developers to train and implement these models for different business scenarios.

What is a Data Scientist?

A data scientist is a person who uses statistical methods, machine learning, data mining, and predictive analytics to turn raw data into actionable insights.

Data scientists work in industries ranging from finance to healthcare to government. They use their skills to identify patterns, trends, and anomalies in the data they analyze and help companies make smarter decisions about how they operate.

Machine Learning vs. Data Science

Data science is a field of study that focuses on how to analyze data best. At the same time, machine learning is a subset of data science that uses various algorithms to predict future events based on past performance.

Data science and machine learning are used in the modern world for many different purposes, but there are some critical differences between the two fields. Data science focuses on finding insights from data sets, while machine learning focuses on making predictions about future events based on past performance.

Roles and Responsibilities of ML Engineer vs. Roles and Responsibilities of a Data Scientist.

ML Engineer Roles and Responsibilities

Data Scientist Roles and Responsibilities
Design, develop, maintain and improve machine learning algorithms for the company’s products. Data scientists use various software, including Python and R, to explore and visualize data.
Make sure the company uses machine learning principles in its business decisions. They are also responsible for ensuring that their insights are adequately communicated to non-technical stakeholders.
The primary responsibility of an ML Engineer is to design, develop and implement machine learning models for different problems. They are responsible for collecting and analyzing data and making recommendations for business processes.
Another responsibility of an ML Engineer is to test and evaluate their machine learning models and algorithms on real-world data sets. Data scientists should be able to write code and use statistical software like R or Python. They must be able to work with a wide range of data sets, from social media analytics to healthcare information.

 

Key Functions of ML Engineers and Data Scientists

The demand for machine learning engineers and data scientists has grown significantly in recent years due to the rise of artificial intelligence and big data analytics.

Functions of ML Engineers

Functions of Data Scientists
Machine learning engineers use algorithms to create models based on data. Then they can use those models to decide how to process new information. There are many functions of data scientists. First, they must be able to collect and analyze data. They also must know how to use different tools, such as programming languages and statistical software.
The Machine learning engineer supports Data Scientists by building data pipelines, implementing models, and training the models based on the results of experiments conducted by Data Scientists. They must have a solid understanding of the business environment in which they work, so they can help their organization make better decisions.
Another critical function of ML engineers is evaluating new data sources for training models and developing new features for existing models. Data scientists are responsible for assisting companies in using AI to optimize their business practices.
They are responsible for ensuring that AI systems can handle complex tasks like facial recognition and other types of pattern-matching, speech synthesis, and language translation. They also design algorithms that can be used in applications like driverless cars or voice-controlled devices. They also work with machine learning algorithms that can help companies understand their internal processes better.

Skills Required of Machine Learning Engineers and Data Scientists

Skills Required of ML Engineers

Skills Required by Data Scientists
The Machine Learning Engineer uses tools like Git/Github for version control, Jenkins for continuous integration, and Docker for containerization. They must also be familiar with machine learning algorithms and statistical modeling techniques for data analysis.
They’ll often be the ones who design and implement new algorithms and methods, as well as improve existing ones. They must communicate technical concepts in a way non-technical people can easily understand.
Machine learning engineers need to be able work with a wide range of tools and skills, including Python, C++, R, and SQL. Data scientists are problem solvers who enjoy working on complex problems and finding solutions using advanced techniques.
They also need to understand statistics, probability theory, and computer science fundamentals like algorithms and data structures. They should also be familiar with machine learning techniques and have some exposure to software development.

Average Salary for Data Scientists and ML Engineers

The average salary for Data Scientist and Machine Learning Engineer in India is ₹ 12.5 Lakhs per year.

Data scientist professionals with less than two years of experience earn an average salary of ₹ 4.4 Lakhs per year.

An average salary of 52.2 lakhs is made by data scientists with more than eight years of experience.

Choose the Right Course

Unlock your potential with Simplilearn’s industry-leading programs! Choose the right path for your career growth with our PG Program in Data Science, or AI and ML Certification Course. Gain hands-on experience, learn from experts, and stand out in the competitive world of data science and analytics. Explore the programs!

Program Name

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Course Duration 11 Months 11 Months
Coding Experience Required No No
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Additional Benefits Upto 14 CEU Credits Caltech CTME Circle Membership Upto 14 CEU Credits Caltech CTME Circle Membership
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Conclusion

Simplilearn’s Post Graduate Program in Data Science is the perfect opportunity to learn from the best. The IBM-sponsored program includes hackathons, masterclasses, webinars, and Ask-Me-Anything sessions. You’ll get hands-on experience with technologies like R, Python, Machine Learning, Tableau, and more!

This course will give you a deeper understanding of how to solve real-world problems with data and analytics. Improve your skillset by learning from other practitioners and Machine Learning Engineers.

FAQs

1. Who earns more, data scientists or machine learning engineers?

In recent years, data science has become one of the most sought-after professions in the world. The average salary of a Machine Learning Engineer is more than that of a Data Scientist. In the United States, it is around US$125,000; in India, it is ₹875,000.

2. Can a data scientist become a machine learning engineer?

Yes, absolutely. A data scientist is essentially a person who uses machine learning to build models and algorithms for solving problems, which then turn into insights that companies and other organizations can use. A machine learning engineer is someone who makes these algorithms, as well as works on improving them as they run in production.

3. Is machine learning harder than data science?

No, machine learning is not more complex than data science.

The two fields are closely related and have many overlapping skills. Data scientists can learn to implement machine learning models and vice versa. Many data scientists and machine learning engineers have a background in statistics or mathematics.

4. Should I learn ML first or data science?

Let’s get the difference between machine learning (ML) and data science out of the way.

Data science is about extracting knowledge from data, focusing on business problems. ML is the subset of data science that uses algorithms to find patterns in data.

So if you want to do data science, you’ll need to learn both, but if you want to do ML alone, it’s unnecessary to learn all data science.

5. Should I go for AI or data science?

AI and data science are complementary technologies that improve your business. AI is great for automating processes and making decisions on the fly; data science helps you understand how people use your product so that you can make informed decisions about how to improve it.

6. Will AI replace data science?

AI is great, but it’s not going to replace data science.

Data science is about understanding what the data means and how it can be used to tell you something about the world. AI is about understanding how to use algorithms and computers to find patterns in data that are meaningful or useful.

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Doctor visit helper

Prepare before seeing a doctor

A simple rural-patient checklist to help you explain symptoms clearly, ask better questions, and avoid unsafe self-treatment.

Safety note: This is not a prescription or diagnosis. For severe symptoms, pregnancy danger signs, children with serious illness, chest pain, breathing difficulty, stroke-like weakness, or major injury, seek urgent care.

Which doctor may help?

Start with a registered doctor or the nearest qualified health center.

What to tell the doctor

  • Write when the problem started and how it changed.
  • Bring old prescriptions, investigation reports, and current medicines.
  • Write allergies, pregnancy status, diabetes, kidney/liver disease, and major past illnesses.
  • Bring one family member if the patient is weak, elderly, confused, or a child.

Questions to ask

  • What is the most likely cause of my symptoms?
  • Which danger signs mean I should go to hospital quickly?
  • Which tests are necessary now, and which can wait?
  • How should I take medicines safely and what side effects should I watch for?
  • When should I come for follow-up?

Tests to discuss

  • Vital signs: temperature, pulse, blood pressure, oxygen saturation
  • Basic physical examination by a clinician
  • CBC, urine test, blood sugar, or imaging only when clinically needed

Avoid these mistakes

  • Do not use antibiotics, steroid tablets/injections, or strong painkillers without proper medical advice.
  • Do not hide pregnancy, kidney disease, ulcer, allergy, or blood thinner use.
  • Do not delay emergency care when danger signs are present.

Medicine safety and first-aid guide

This section is for patient education only. It does not replace a doctor, pharmacist, or emergency care.

Safe first steps

  • Rest, drink safe water, and observe symptoms carefully.
  • Keep a written note of symptoms, duration, temperature, medicines already taken, and allergy history.
  • Seek medical care quickly if symptoms are severe, worsening, or unusual for the patient.

OTC medicine safety

  • For mild pain or fever, ask a registered pharmacist or doctor before using common over-the-counter pain/fever medicines.
  • Do not combine multiple pain medicines without advice, especially if you have kidney disease, liver disease, stomach ulcer, asthma, pregnancy, or take blood thinners.
  • Do not give adult medicines to children unless a qualified clinician advises it.

Avoid these mistakes

  • Do not start antibiotics without a proper medical decision.
  • Do not use steroid tablets or injections casually for quick relief.
  • Do not delay emergency care because of home remedies.

Get urgent help if

  • Severe symptoms, confusion, fainting, breathing difficulty, chest pain, severe dehydration, or sudden weakness need urgent medical care.
Medicine names, dose, and timing must be decided by a qualified clinician or pharmacist after checking age, pregnancy, allergy, other diseases, and current medicines.

For rural patients and family caregivers

Patient health record and symptom diary

Write your symptoms, medicines already taken, test results, and questions before visiting a doctor. This note stays on your device unless you print or copy it.

Doctor to discuss: Doctor / qualified healthcare provider
Tests to discuss with doctor
  • Basic vital signs: temperature, pulse, blood pressure, oxygen level if needed
  • Relevant blood, urine, imaging, or specialist tests only after clinical assessment
Questions to ask
  • What is the most likely cause of my symptoms?
  • Which warning signs mean I should go to emergency care?
  • Which tests are really needed now?
  • Which medicines are safe for my age, pregnancy status, allergy, kidney/liver/stomach condition, and current medicines?

Emergency warning signs such as chest pain, severe breathing difficulty, sudden weakness, confusion, severe dehydration, major injury, or loss of bladder/bowel control need urgent medical care. Do not wait for online information.

Safe pathway to proper treatment

Care roadmap for: Machine Learning Engineer vs. Data Scientist

Use this simple roadmap to understand the next safe steps. It is educational and does not replace examination by a doctor.

Go to emergency care if you notice:
  • Severe or rapidly worsening symptoms
  • Breathing difficulty, chest pain, fainting, confusion, severe weakness, major injury, or severe dehydration
Doctor / service to discuss: Qualified healthcare provider; specialist depends on symptoms and examination.
  1. Step 1

    Check danger signs first

    If danger signs are present, seek emergency care and do not wait for online information.

  2. Step 2

    Record the symptom story

    Write when symptoms started, severity, medicines already taken, allergies, pregnancy status, and test results.

  3. Step 3

    Visit a qualified clinician

    A doctor, nurse, or qualified healthcare provider can examine you and decide which tests or treatment are needed.

  4. Step 4

    Do only useful tests

    Do tests after clinical assessment. Avoid unnecessary tests, random antibiotics, or repeated medicines without diagnosis.

  5. Step 5

    Follow up and return early if worse

    If symptoms worsen, new warning signs appear, or treatment is not helping, return for review quickly.

Rural patient practical tips
  • Take a written symptom diary and all previous prescriptions/test reports.
  • Do not hide medicines already taken, even herbal or over-the-counter medicines.
  • Ask which warning signs mean urgent referral to hospital.

This roadmap is for education. A real diagnosis and treatment plan requires history, examination, and clinical judgment.

Internal learning pathway

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  2. RAG Evaluation and Meta-Evaluation with GroUSE DefinitionThis tutorial introduces GroUSE, a framework for evaluating Retrieval-Augmented Generation (RAG) pipelines, focusing on the final…
  3. Deep Evaluation of RAG Systems using deepeval DefinitionThis code demonstrates the use of the deepeval library to perform comprehensive evaluations of Retrieval-Augmented Generation (RAG) systems.…
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