Understanding Different Job Roles in the field of Data Science

The relation between skillset and job roles

It’s very common these days to come across these terms — data science, artificial intelligence, machine learning, deep learning, and much more. As a consequence, a bunch of different data science roles came our way, but it’s tough to get a general understanding of how they differ and the skills they require.

This article aims to present brief insights of different job roles, responsibilities of each title along with the skills/qualifications required and estimated salary earned in different data science job roles. Let’s get started then.

The Essential Dilemma:

There are major four job roles:

1. Data Engineer
2. Data Analyst
3. Data Scientist
4. ML Engineer

Source: Google Trends

Current trends on these four job roles according to Google Trends.

But do you know which one is the right to kickstart your career and why you should choose one over the other? Let’s dive into more details

Data Engineer:

We are in the age of the data revolution. Prominent enterprises now base their decision-making skills on insights derived from the analysis of data. For many organizations, data engineers are the first hires on a data team. Data Engineers are the ones who gather data from other websites through web scrapping, API’s or IoT devices and ingests the data into the data warehouse. Examples of data warehousing systems include Amazon Redshift or Google Cloud. Finally, Data Engineers create ETL (Extract, Transform and Load) processes to make sure that the data gets into the data warehouse.

For example, think of a song recommender system machine learning model that needs to be deployed as part of a company’s application to make the application better. Each time a user plays a song, a new piece of data is being created. A Data Engineer would define how to collect this data, what types of metadata should be appended to each click event, and how to store the data in an easy-to-access format

The crucial tasks included in Data Engineer’s job role are:

• Collect data through web-scraping, API’s or IoT devices.
• Ingest the collected data into a data warehouse.
• Manage ETL(Extract, Transform & Load) process
• Introducing new data management tools & data models for easy access to the data.

Technical Aspects:

• Experience with big data tools: Hadoop, Spark, Kafka, etc.
• Experience with relational SQL and NoSQL databases, including Postgres and Cassandra
• Experience with AWS cloud services.
• Experience with object-oriented/object function scripting languages like Python, Java, C++, Scala, etc.

Job Scenario:

Data engineers have an annual salary growth of about 9%. The average starting salary of a big data engineer can range from INR 6,00,000 to INR 10,00,000. According to a survey performed by the Internal Revenue Service (IRS), the top salary bracket makes big data engineers the top 5% of the highest-earning roles. An increasing number of enterprises have now started adopting data in their projects, while others have already made plans to incorporate data into their future projects. The sports industry, for instance, has an increased demand for data engineers to track metrics of consumers like social media behavior, ticket-purchasing habits, demographics, brand interests, and psychographic profiles.

Data Analyst:

The Data Analysts, perform exploratory data analysis, run statistical analysis and creates visualization based on analysis and forwards further to perform suitable algorithms to train the model. Analysts implement feature engineering, feature selection, clean the data using programming languages, spreadsheets, and business intelligence tools to describe and categorize the data.

For example, Incase of a music recommendation integrating model, a Data Analyst would create visualizations to track which artist is played the most, the genres of songs which are played the most and how much money the company is making.

The crucial tasks included in Data Analyst’s job role are:

• Managing master data, including creation, updates, and deletion.
• Processing confidential data and information according to guidelines.
• Create reports and analysis.
• Managing and designing the reporting environment, including data sources, security, and metadata.
• Providing expertise in data storage structures, data mining, and data cleansing.

Technical Aspects:

• Structured Query Language (SQL)
• Data mining, Cleaning, and Munging
• R or Python-Statistical Programming(Numpy, pandas library)
• Data Visualization and Presentation Skills (Tableau, Power BI, seaborn library in python)
• SQL
• Data Story Telling

Job Scenario:

More than 97,000 analytics positions remain vacant in India due to the shortage of talent. 11 lakhs is the average salary in analytics and data science.BFSI sector has the maximum demand for data science skills in India followed by e-commerce and telecom. Python continues to be the tool of choice among data analysts and data scientists and this is reflected in the hiring market as well with 17% jobs listing the language as a core capability.

Data Scientist:

It is considered as the sexiest job of the 21st century. They work based on the visualization provided by the data analytics team to build and optimize classifiers using machine learning techniques.

‘Data Scientists know more about statistics than any software developer and know more about software development than any statistician.’ — Josh Wills, Director, Slack.

For example, A Data Scientist would take the data on the artists, music genres and train the machine learning to recommend songs/albums for each user.

The crucial tasks included in the Data Scientist’s job role are:

• Thoroughly clean data to discard irrelevant information and prepare the data for preprocessing and modeling
• Perform exploratory data analysis (EDA) to determine how to handle missing data.
• Discovering new algorithms to solve problems and build programs to improve current strategies.
• Perform feature engineering, feature selection to implement analytical methods, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling

Technical Aspects:

• Programming
• Applying Machine Learning algorithms and libraries(Scikit Learn, Tensorflow, PyTorch)
• Data Visualization and Reporting
• Statistical analysis and Math
• Effective Communication
• Software Engineering Skills
• Data Mining, Cleaning, and Munging

Job Scenario:

Data Scientists can also work as data analysts which makes Data Scientists as one of the highest-paid jobs. Data Scientists ranked first among the most promising jobs in the United States in 2019 as per LinkedIn’s report. According to the same report, Data Scientists averaged US$130,000 in the basic salary this year. Furthermore, the job vacancies increased by 56 percent from the last year. Currently, there are more than 4,000 Data Science vacancies across the country. No wonder: Data Scientists are led to the top as the best job in the United States, thanks to its high demand, high salaries, and high job satisfaction. Also, Analytics India Magazine predicts that the demand for Data Science professionals in India will increase sevenfold in the next seven years and the market will reach US$20 billion.

Machine Learning Engineer:

It’s the final stage of a data science project cycle where the model created is deployed to be integrated into the application or website. Machine Learning Engineers are neither experts in Data Science nor web development. They ideally use both the knowledge to deploy the final model. The work of a Machine Learning Engineer is to bridge the gap between Data Scientist’s work and production environment. A Machine Learning Engineer is more concerned with deploying production-ready models.

For example, Machine Learning Engineer deploys the song recommender machine learning model to a production-ready environment. It involves different types of engineering work such as integrating the model to a software system, optimizing the model for performance and scalability, and re-training it with new data, monitoring, and maintenance the ML system

The crucial tasks included in the ML Engineer’s job role are:

• Removing errors from data sets and find correct data representation methods
• Deploying the machine learning model to be integrated into the application/ website
• Scaling and optimizing the model for production
• Monitoring and maintenance of deployed models

Technical Aspects:

1. Probability and statistics
2. Data Modeling and Evaluation
3. Applying Machine Learning algorithms and libraries(Tensorflow, Pytorch)
4. Software Engineering and system design(AWS, Azure)

Job Scenario:

As a fresher, there is a median salary of almost 13 Lakhs and rising for a Machine Learning Engineer.

This is one of the trendiest and the coolest jobs to have as per a survey conducted earlier this year.

A Machine learning engineer in the USA gets an annual pay of about 140 thousand dollars. It’s about 50,000 pounds in the UK and about 13 Lakhs in India.

CONCLUSION:

I hope you’ve got a brief idea about the insights of these job roles. Now, it’s all about selecting the right profession to kickstart your career which suits your skills and interests. The upward swing in Data Science career opportunities is expected to continue for a long time to come. As data pervades our life and companies try to make sense of the data generated, skilled Data Scientists will be continued to be wooed by businesses big and small.

All the best for your career and job search in Data Science!