Machine Learning Engineer vs Data Scientist
The roles of machine learning engineer and data scientist are similar, considering that both positions need certain defined qualifications, work on similar technologies, and handle huge data sets. The article tries to cover the differences between machine learning engineers and data scientists.
Did you know that over 2.5 Quintillion bytes of data are generated daily across the globe! IDC predicts that by 2025, there will be 175 zettabytes of data, marking a growth of 61%. To manage such humongous volumes of data and get desirable conclusions, organizations need experts. Machine learning engineers and data scientists are two of the most trendy job profiles in the world of data right now. Often there is confusion around the roles of these two. In this article, we discuss machine learning engineer vs data scientist, their job roles, and difference in skills and eligibility.
Content
Best-suited Machine Learning courses for you
Learn Machine Learning with these high-rated online courses
Who is a Data Scientist?
Data scientists are the professionals responsible to manage and find trends in messy, unstructured data. Such data may come from a range of sources like social media, online news, huge datasets, emails, mobile devices, to name a few. Data scientists have an impeccable command over mathematics, statistics, computer science, data analysis, and programming.
Their expertise is now required in almost all sorts of industries with the aim of obtaining reliable solutions to everyday problems. For example, determine user behavior, predict sales of a product, forecast performance of campaigns, among others.
Who is a Machine Learning Engineer?
A machine learning engineer is responsible for creating programs and algorithms that enable machines to perform specific tasks. These programs and algorithms allow machines to perform actions without being specifically told to do those tasks and improve from their experience. The machine learning engineer needs to have a substantial set of skills in algorithm development and ML design. They should know how these technologies work, how to work with data and be able to contribute to the full life cycle of an ML project.
Machine Learning Engineer vs Data Scientist
- Job roles
- What Does it Take to Become a Machine Learning Engineer?
- What Does it Take to Become a Data Scientist?
- Salaries
- Top Employers
Job roles
Let’s discuss some of the crucial roles of both machine learning engineers and data scientists –
Machine Learning Engineer Job Role
A machine learning engineer is required to perform the below job functions –
- Improve the efficiency of ML systems and algorithms using statistics, modeling, and machine learning
- Analyze data to make appropriate product recommendations and design A/B experiments
- Design solutions by choosing the right algorithms, features, and hyperparameters
- Manage the full lifecycle of ML Models – data preparation, model creation, and deployment
- Integrate information from data sources like Databases, Data Warehouses, External ML Systems/Algorithms, etc. and enrich models features
- Explore and visualize information obtained from the data and gain an understanding of it
- Define validation strategies, preprocessing or feature engineering to be done on a given dataset
- Define data augmentation pipelines
- Analyze the errors of the model and designing strategies to overcome them
- Deploy ML models to production
Data Scientist Job Role
Some of the key on-job tasks of a data scientist include –
- Drive data backed decisioning for customers wrt products, processes, offer strategies, channel selection, timing etc.
- Manage huge and complicated data sets
- Apply analytical methods to solve data-based, non-routine analysis problems
- Design and perform data science experiments
- Perform data gathering, cleaning, processing, analysis, and visualization
- Build and prototype analysis pipelines
- Explore insights from the data analysis
- Translate the insights into actionable recommendations influencing product roadmaps
- Implement the solution and monitor the performance
- Work with cross-functional teams to identify and solve business problems
- Create metrics to evaluate performance of the deployed solutions
- Analyze data extensively and communicate findings to senior management
What Does it Take to Become a Machine Learning Engineer?
To build a career in machine learning, you would need to meet the following criteria/skillsets –
- Postgraduate or Ph.D. in computer science/mathematics/statistics/machine learning/neural networks/deep learning/engineering/equivalent
- Working knowledge of programming languages like C++, Java, Python, R, Lisp, and Prolog, etc.
- Exceptional math and statistics skills
- Know how to sift through datasets, identify patterns in data and draw meaningful conclusions from it
- An analytical mindset with a flair to solve problems using your practical understanding
- Experience in working with ML frameworks and libraries like TensorFlow, NumPy, SciPy, Scikit-Learn, Theano, Streamlit, etc.
- Data structures, data modeling, and software architecture
- Knowledge of computer architecture
Must Read – How to Become a Machine Learning Engineer
What Does it Take to Become a Data Scientist?
- Postgraduate or Ph.D. in computer science/mathematics/statistics/engineering/equivalent
- Ability to design information reporting systems
- Knowledge of Machine Learning, Artificial intelligence and cloud computing
- Ability to manage software tools in data structure and manipulation systems (data wrangling, data munging or data tyding)
- Predictive modeling and distributed computing
- Knowledge of Python, Hadoop, Hive, BigQuery, AWS, Spark, SQL Database/Coding, Apache Spark, among others
- Statistical methods and packages, multivariable calculus, linear algebra, etc.
- Ability to write codes and manage big data chunks
- Knowledge of data warehousing and business intelligence platforms like Google Marketing Platform (Enterprise), Microsoft Power BI, Oracle Analytics Cloud, Qlik, Tableau, etc.
- Python libraries like TensorFlow, NumPy, SciPy, Pandas, Matplotlib, Keras, SciKit-Learn, PyTorch, etc.
Salaries
Salaries depend on the experience, skillset of the individual, company as well as the location they are working in. They may vary for different professionals with different work profiles.
Machine Learning Engineer Salary
Ambitionbox suggests that the average salary of a Machine Learning Engineer in India is Rs. 7.3 Lakh.
Data Scientist Salary
The average salary of a data scientist in India is Rs. 10.9 LPA, as per Ambitionbox.
You May Like – Data Scientist Salaries – Your Ultimate Guide
Top Employers
As per the Naukri database, below are the top employers for machine learning engineers and data scientists –
Top Employers – Machine Learning
Top Employers – Data Science
Conclusion
ML engineers focus on building and managing machine learning models and systems, data scientists extract meaningful insights from large data sets. Both profiles are among the most sought after and highly paid these days.
If you wish to seek a career in data science or machine learning then consider it to be high time. Take up relevant data science courses or machine learning courses as per your skillset, and get started!
Top Trending Articles:
Data Analyst Interview Questions | Data Science Interview Questions | Machine Learning Applications | Big Data vs Machine Learning | Data Scientist vs Data Analyst | How to Become a Data Analyst | Data Science vs. Big Data vs. Data Analytics | What is Data Science | What is a Data Scientist | What is Data Analyst
Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio
Top Picks & New Arrivals
