What Does A Data Scientist Do: Skills, Roles And Responsibilities, Salary, And More

The U.S. Bureau of Labor Statistics mentioned data science in the top 20 fastest-growing occupations and has predicted 31% growth over the next 10 years.

Source: bls.gov

According to the U.S. Bureau of Labor Statistics, the number of jobs in the data science field will increase by about 28% through 2026, with roughly 11.5 million new jobs in the field.

While demands are increasing in the field, there is still a lot of confusion and less clarity about data science and jobs related to that field. One thing is clear amidst this uncertainty – the growing importance of Big Data and a data-driven environment across industries.

This is our second post in the series of guides on data-centric job titles. We have covered the nitty-gritty of the data engineer role, and in this post, we are covering the roles and responsibilities of data scientist, skills, salary, and interview questions.

Let’s begin with the most basic question.

What Is A Data Scientist? What Does It Mean To Be A Data Scientist?

Data science is a broad term and includes methods, processes, algorithms, and systems to analyze data and extract hidden insights from the data (structured or unstructured).

A data scientist is a person who knows how to extract insights from the data by using various processes, methods, systems, and algorithms. Data scientist requires a range of skills to analyze, interpret, and visualize data to make informed decisions.

Glassdoor mentioned data scientist as the #2 best job in the United States.

Source: glassdoor.com

Data Scientist is a high-ranking professional in the big data world who possesses mathematical, statistical, scientific, analytical, and technical skills to clean, prepare, and validate structured and unstructured data in order to make better decisions for businesses.

Data Scientists are the persons who ask questions to data. And they must know how to form those questions using analytical, statistical, machine learning, scientific methods, and various tools.

As businesses seek to leverage the power that data has to offer, organizations, be it small or large, hiring data scientists to accelerate their growth through data-driven decision-making.

Data Scientist Roles And Responsibilities: What Does A Data Scientist Do?

Before we begin to classify the roles and responsibilities of data scientists, have you ever thought about why they are called scientists?

Science is all about research and discoveries and that’s what data scientists do. They tell people what data is hiding and how those hidden insights can be applied to business processes for better performance and increased ROI.

They collect data, conduct a wide range of experiments using different models and methods, understand the result, predict impact, and communicate the same to their peers in the organization. Their skills must not be confined to only analytical, statistical or managerial. That’s where they are different. They need special skills that make them different from data engineers, analysts, and other data-centric roles.

The roles and responsibilities of data scientists greatly vary based on the organizations’ needs. Overall, they require to fulfill several or all responsibilities mentioned below:

• Gather data and identify sources of data
• Analyze a large amount of structured and unstructured data
• Create solutions and strategies that address business challenges
• Collaborate with team members and leaders to design data strategies
• Combine different algorithms and modules to discover trends and patterns
• Present information through various data visualization techniques and tools
• Explore more technologies and tools to create innovative data strategies
• Create end-to-end analytical solutions from data collection to presentation
• Help building data engineering pipelines
• Assisting team of data scientists, BI developers, and analysts in their projects whenever required
• Working with the sales and pre-sales team in cost reduction and effort estimation as well as cost optimization
• Stay updated with the latest tools, trends, and technologies to improve overall efficiency and performance
• Working closely with product team and partners to present data-driven solutions built with innovative ideas
• Design analytics solutions for businesses using various tools, applied statistics, and ML
• Lead discussions and check implementation feasibility of the AI/ML solutions concerning business processes and outcomes
• Architect, implement, and monitor data pipelines and conduct knowledge sharing sessions to the peers for effective use of data

The role of a data scientist depends on how organizations are using data science in their strategies and models.

Now, if we talk about required skills to become a data scientist, apart from strong data and computational focus, they require many other skills.

Data Scientist Skills:

In the most basic sense, data science is about combining the best models, algorithms, and tools to get the job done.

Here’s the list of skills for a data scientist:

Mathematics And Statistics:

Many data scientists come from computer science, math, or stat background. Knowledge of statistics, probability, and math is a primary requirement when you want to work as a data scientist. Data scientists are expected to work on different machine learning algorithms, hypotheses, and models to make decisions and offer suggestions. It is one of the foundational skills for data scientists.

Machine Learning:

At its core, data science is a field of study that involves a scientific approach to extract knowledge from data. They may not require expertise in machine learning but should be familiar with basic concepts and models of machine learning. Most of the data science methodologies are based on machine learning in a way or the other.

Programming Skills:

A data scientist requires strong practical knowledge in R or Python – two leading programming languages for data science applications. They must be familiar with coding, database, and software development lifecycle to build a solution that meets the needs. They require expertise in programming language and major concepts.

Analysis And Visualization:

If you can’t understand data, you can’t make a career in the data field. Data analysis and data visualization are key skills to become a data scientist. You must have the intellectual curiosity to see beyond data and discover trends, patterns, and KPIs in an easy-to-understand, visually appealing format. Also, they must possess knowledge of various data visualization and data analytics tools and methodologies to convert data into meaningful insights.

Database Management:

Being a data scientist not only requires proficiency in machine learning or statistical models but also requires sound knowledge of databases and data management. They need to manage a large volume of data, integrate, clean, structure it, and prepare data to be used further. They require strong expertise in MySQL, SQL Server, Oracle, PostgreSQL, and other non-relational databases such as MongoDB, DynamoDB, Casandra, Redis, etc.

Software Engineering Skills:

Without knowledge of how the software works, data scientists might have difficulty in work. Apart from experience in developing R and Python, they must know modern approaches to software development and their impact. DevOps, continuous integration and deployment, experience in cloud computing are everyday skills to manage and process data.

Several other skills that are important for a data scientist:

• Years of experience as a data scientist, data analyst or data engineer
• Experience in data mining, data modeling, and reporting
• Familiarity with machine learning and operation-research models
• Experience using various data visualization and data management tools
• Problem-solving attitude and analytical mind
• Excellent verbal, written, and presentation skills
• Understanding of various business domains and business acumen
• Storytelling skills and ability to effectively communicate results to the team
• Ability to quickly adapt to constantly changing requirements
• Proficiency in Excel for data management and manipulation
• Experience in statistical modeling techniques and data wrangling
• Able to work independently and set goals keeping business objectives in mind

Data scientist is one of the highest-paying jobs in the world as it requires out-of-the-box thinking and a combination of computational, analytical, and scientific skills. The market for data scientists is competitive and they are difficult to hire given rare qualities and less number of professionals.

Data scientists must have technical and analytical skills. But, apart from that, there are several soft skills that are essential for every data scientist. These foundational soft skills help aspiring data scientists stand out from the crowd. These skills are not taught anywhere. However, these skills can be improved and help them thrive in today’s market.

These skills are not listed or even don’t require formal certifications but these are the must-haves for data scientists.

Top Soft Skills For Data Scientist

• Critical Thinking:

It not only relates to data scientists, it applies everywhere. Critical thinking is one of the important skills for every profession. It refers to questioning, analyzing, conceptualizing, and evaluating information or any data generated by observance, experiences, reflection, analysis, or communication.

It is more of an intellectual process and is also influenced by certain habits such as clarity, accuracy, consistency, precision, depth, and relevance.

For data scientists, this is even more important to frame questions, identify insights, and understand how results can be applied to take further courses of action. Critical thinking in data science is one of the most essential skills to see all angles of a problem, objectively analyze, and interpret results.

• Communication

Communication is the key to success. Effective communication makes things easier for data scientists. Whether you are starting your career or an experienced professional, you must be able to convey the right terms at right time. This makes a bigger difference.

The role of a data scientist is to analyze, process, and model data, and then interpret the results. Data scientists must clearly explain these results to technical and non-technical teams. Use of correct terms, correct approach, and fluent explanation of results by data scientists help increase data literacy across the organization.

• Adaptability

Technological innovations are happening every day. Data scientists must act proactively and continuously learn new trends, techniques, and concepts in the data science field. They need to quickly adapt to the new technologies to meet changing business demands.

• Curiosity

Data Science is a wide field and it is not limited to traditional ways of reporting and analytics. Being curious about data help data scientists uncover hidden insights, patterns, and correlation.

Intellectual curiosity is one of the critical skills of data scientists that motivate them to dive deeper into results and go beyond initial assumptions. They must possess the curiosity to look beyond the results and carry on the search to know more. Successful data scientists never settle for less; instead, they constantly seek the ‘Why’ behind every data story.

• Business Skills

Data scientists must understand the business and problems they are trying to solve. They must be well acquainted with business and processes to solve existing problems and turn data into insights that drive future growth.

Data Science combines multiple skills rather only having knowledge about their field. They must deeply understand the business to identify opportunities and avoid risks using the existing resources.

The ultimate objective of a data scientist is to identify current problems and put data to work for getting the best results. Data-driven strategies in line with business objectives make companies reach greater heights. This is how data scientists can drive future growth.

Data Scientist Salary: How Much A Data Scientist Earns?

Data scientists are high in demand and expensive to hire. Being a data scientist in 2021 and the upcoming years means a high pay scale and a rewarding career with bright future scope.

Data Scientists Salary In Different Parts of the World:

In the UK, the average data scientist’s salary is £40,443 as per Payscale.

The average salary for a data scientist in the United States is $96,528. Source: payscale.com

In India, this number is ₹824,241.

Source: payscale.com

As per Glassdoor salaries, the national average salary for a data scientist is $1,15,602 per year in the United States.

According to Glassdoor, the national average salary for a Data Scientist is ₹10,00,000 per year in India.

For an entry-level data scientist, the average salary in India is ₹511, 468. An employee with 5-9 years of experience can get somewhere around INR 12-14L. As per Payscale, the average mid-level data scientist salary is ₹1,367,306.

A highly experienced professional who is able to lead teams can earn as high as INR 24L per year. This is one of the lucrative job titles where more years of experience will give you a high salary and so on.

Salaries are varied based on factors such as location, experience, skills, and company.

Now, we have got answers to every question related to roles and skills, let’s see how to become a data scientist. Universities are offering graduation, post-graduation, and master’s degree programs in data science along with languages and machine learning. Companies are conducting their learning programs and certifications to spread the word about data science and its importance across industries. This will help aspiring students and professionals to learn data science and make a career in this field.

If you are planning to become a data scientist or simply want to enhance your career, we have combined a list of data scientist interview questions. This includes basic as well as technical concepts that you may face in the interview.

Data Scientist Interview Questions:

• What is the difference between supervised learning and unsupervised learning? Explain with examples
• What is logistic regression in Data Science?
• Explain the decision tree algorithm
• What are likelihood and prior probability?
• What are the disadvantages of using the linear model?
• What is a Linear Regression?
• Explain the difference between the expected value and the mean value.
• Can you explain Recommender System?
• What do you mean by Interpolation and Extrapolation?
• Explain cross-validation
• What is the aim of A/B testing?
• What is a Random Forest? Why is it good?
• Explain the K-means clustering method?
• How can you select important variables while working on a dataset? Can you explain?
• What are the types of biases that can occur during sampling?
• How can you calculate accuracy using a confusion matrix? (given confusion matrix)
• What is the significance of the p-value?
• How will you deal with a dataset consisting of variables with more than 30 percent missing values?
• How to check if the regression model fits the data well?
• Explain the Kernel Trick.
• What is data science?
• Do you know probability fundamentals? Can you explain?
• What are confounding variables?
• What is the difference between a box plot and a histogram?
• Explain neural network fundamentals.
• What are the statistical techniques used in data science? Name them
• What is association analysis? Where it is used?
• Explain collaborative filtering.
• What are the popular libraries used in Data Science?
• How are machine learning and data science related to each other?
• Explain univariate, bivariate, and multivariate analysis.
• Which language do you prefer – R or Python and why?
• What is Naïve in Naïve Byes algorithm?
• Explain false positive and false negative.
• What is dimension reduction and why it is important?
• What is Ensemble Learning?

Also, there will be some practical questions where you need to apply different methods or algorithms or solve the problem using statistical formulas.

Data Scientist: A Rising Career, Rewarding Job Of The Decade

More and more organizations have started recognizing the power of big data and data analytics. Data drives today’s world and that’s the reason why we see a surge in data-centric roles across the world. Data is crucial for every organization and it must be leveraged to make timely and effective decision-making.

In this fast-evolving world, data is more valuable than you think. Data-centric roles have many things in common but every role has a different duty to fulfill and a unique role to contribute to organizational growth. If you are on the way to become a data scientist, we hope that this guide helps you find answers to your questions. If you want to hire one, we can help you find the right skill set that meets your goals!