Data Scientist Vs. Machine Learning Engineer: Job Role and Salary

Choosing a particular job role between Data Scientist vs Machine Learning Engineer can be challenging, as both fields are high in demand but require different skill sets. Data Scientists specialize in analyzing data and extracting valuable insights, whereas Machine Learning Engineers focus on developing models to make predictions and automate tasks. However, despite the distinct skill set and responsibilities, the choice of job role is based on your interests and strengths.

Let’s understand in-depth on the factors that differentiate these two data-centric careers.

Who is a Data Scientist?

A Data Scientist is a professional who specializes in extracting insights from vast amounts of data using various statistical, analytical, and machine-learning techniques. They are skilled at collecting, cleaning, and organizing data from multiple sources to identify patterns and develop predictive models for business problems. 

Their role involves the usage of programming languages such as Python, R, or SQL to manipulate data and derive actionable insights to support decision-making processes. Data Scientists are also proficient in data visualization techniques to communicate their findings effectively to the stakeholders. Overall, Data Scientists play a crucial role in utilizing data and driving business strategies to explore new opportunities for expansion and innovation across diverse industries.

Who is a Machine Learning Engineer?

A Machine Learning Engineer specializes in designing, implementing, and deploying machine learning models and systems. They build predictive models, train them, and tune them to run on massive datasets. With the help of machine learning techniques, they develop solutions for complex problems and work closely with Data Scientists, Data Engineers, and domain experts. 

Machine Learning Engineers also deploy models in production and assess machine learning models' performance using metrics like Root Mean Squared Error, Area Under the ROC Curve, Cross-Validation, etc. 

Data Scientist vs Machine Learning Engineer: A Quick Comparison

Both Data Scientists and Machine Learning Engineers contribute to extracting valuable insights from data. However, their areas of expertise and daily responsibilities may vary. Here’s a quick comparison between the two roles.

Data Scientist Vs Machine Learning Engineer: In-depth Comparison

Both Data Scientists and Machine Learning Engineers require strong analytical skills to work with their data, but their specific job roles, salaries, and skill sets have some key differences.

Here’s an in-depth comparison between a Machine Learning Engineer vs. Data Scientist:

Job Role

Here are some of the key roles of a Data Scientist: 

• Data Collection and Preprocessing: Gathering data from various sources like databases, APIs, and websites. Preprocessing the gathered data to ensure accuracy and consistency by handling missing values and removing outliers.‍
• Exploratory Data Analysis (EDA): EDA is crucial in understanding the structure, patterns, and relationships within the data using statistical methods and visualization techniques.‍
• Feature Engineering: Creating new features or variables from the existing data to ensure better performance of machine learning models.‍
• Model Selection and Evaluation: The important task for any Data Scientist is the selection of an appropriate machine learning algorithm that best suits the existing requirements. Evaluate the chosen model built using relevant metrics like cross-validation to assess the performance.‍
• Communication and Visualization: Communicating the extracted insights to the stakeholders through reports, presentations, and visualizations that are understandable to both technical and non-technical audiences.

Here are some of the key roles of a Machine Learning Engineer:

• Data Preparation and Pipeline Development: Preparing data for machine learning models by cleaning and transforming data.‍
• Model Development and Training: Developing and training machine learning models using appropriate algorithms and frameworks such as Tensor Flow or PyTorch.‍
• Fine-tuning of Parameters: Tuning the parameters of the model to optimize model performance using techniques like grid search and random search.‍
• Deployment and Monitoring: This involves deploying the machine learning models into the production environment and monitoring their performance.‍
• Collaboration: Collaborating with other team members, such as Data Scientists, software engineers, and domain experts, and effectively communicating the requirements in detail.

Salary

The Data Scientist vs Machine Learning Engineer salary can vary based on experience, industry, and location.

• According to Indeed, a Data Scientist's average salary ranges from $80,000 to $125,000 per annum.
• Similarly, based on Indeed research, a Machine Learning Engineer’s average salary ranges from $100,000 to $170,000 per annum. 

Skill Set

The key skills of a Data Scientist are: 

• Expertise in programming languages such as Python or R for data analysis and manipulation.
• Understanding of data warehousing concepts and familiarity with SQL language is essential.
• Strong understanding of deep learning and machine learning algorithms, such as regression, classification, clustering, dimensionality reduction, and more, to solve business problems.
• Proficiency in statistical methods and hypothesis testing to analyze data.
• Strong analytical and problem-solving skills to identify patterns.
  

The key skills of a Machine Learning Engineer include the following:

• Deep understanding of various machine learning techniques and algorithms, including supervised, unsupervised, deep, and reinforcement learning.
• Proficiency in developing and training ML models using libraries such as TensorFlow or Scikit Learn.
• In-depth Python, Java, or C++ knowledge to build production-ready systems.
• Ability to work on version control systems like Git for managing codebase changes and collaboration with team members.

Data Engineer vs Data Scientist vs Machine Learning Engineer

You might get confused by the terms Data Engineer, Data Scientist, and Machine Learning Engineer. Organizations sometimes assign overlapping tasks, but their core functionalities differ. 

Here’s how:

• Data Engineers: They serve as the designers of the data infrastructure and do the groundwork for a resilient data infrastructure. Their proficiency lies in designing, constructing, and managing pipelines responsible for extracting, transforming, and loading data from diverse sources into accessible formats. They lay down a framework that facilitates a smooth flow of data, empowering Data Scientists and Machine Learning engineers to execute their tasks efficiently.
• ‍Data Scientists: They are the wizards of data infrastructure who dig out the data from treasures, preprocess it, and extract valuable insights from it. Equipped with statistical, mathematical, and programming skills, they build models and produce meaningful patterns.‍
• Machine Learning Engineers: Automate data-driven models into practical applications. They refine the prototypes developed by Data Scientists, optimizing them for scalability, efficiency, and real-world implementation.

The Future of Data-Centric Careers

As most industries depend on data for decision-making and automation, the need for Data Scientists and Machine Learning Engineers is expected to stay strong. These positions are leading the way in technological advancement and offer a wide range of opportunities for career advancement and influence.

Whether you specialize in any data science course, the outlook for data-centric careers holds promising opportunities and the potential to influence the evolving digital era.

Streamline Data Engineering Tasks with Airbyte

Regardless of your data-related job role, you would always have to collect data from different sources. To streamline this process, you can use a robust and reliable tool like Airbyte to automate the data integration procedure.

Airbyte serves as a data integration tool, facilitating the connection between diverse sources and destinations. To streamline data synchronization, the platform offers 350+ pre-built connectors. In instances where a connector is unavailable, you can quickly construct a custom one using its Connector Development Kit within minutes.

Some Key Feature’s of Airbyte are:

• Airbyte supports the Change Data Capture feature (CDC), which allows you to capture the incremental changes in the dataset since the last synchronization. This feature is efficient as it reduces the amount of data being transferred and saves processing time.‍
• PyAirbyte, Airbyte’s open-source Python library, consolidates all Airbyte connectors, offering a convenient solution for Python developers to harness Airbyte’s capabilities within their Python scripts.
• Airbyte provides aspiring Data Scientists and engineers with a wealth of resources, including YouTube tutorials, documentation, and a dedicated blog section. These resources cover various topics such as data transformation techniques, crash courses, and data integration best practices, offering valuable insights for establishing a strong foundation.

Conclusion

The comparison of Data Scientist vs Machine Learning Engineer is a fundamental part of the data-driven industry, each with unique focuses and expertise. Data Scientists specialize in transforming raw data into valuable insights that drive business decisions, whereas Machine Learning Engineers develop robust machine learning systems and implement them in practical applications. Both roles present promising opportunities, but the decision ultimately rests on your interests, skill sets, and career goals.