Data Scientist Jobs

In today’s world, data is everywhere. Every time we shop online, use an application, or browse the internet, we generate data. This data has valuable information, but understanding it is not always easy. That is where data scientists come in.

Using their skills in technology, they take all this information and make it understandable for others. They help businesses figure out what their customers want, predict what might be popular in the future, and even solve problems by analysing trends.

The demand for data scientists is increasing with time, making it the right time to join this career. In this article, we will dive into what data scientists do and everything you need to know about this role.  

What is a data scientist?

Data scientists are experts who gather data from various sources, like shopping websites, social media, etc. Then, they analyse this data to find important details, patterns or trends.

For instance, if a business wants to know which shoes are most liked by young people, a data scientist will look at sales records, online comments, reviews, and more to figure it out.

They use special computer tools, statistics and maths skills to do their job. Once they have studied the data, they present it in a way that is easy to digest for others.

All in all, data scientists play a crucial role in helping businesses make smart choices by understanding and using data.

What does a data scientist do?

Collecting data

One of the primary tasks of a data scientist is to gather data. Research reveals data scientists spend around 80% of their time collecting, cleaning, and organising data. They set up processes to automatically collect and store this data for analysis.

Cleaning data

Raw data often have missing values, duplicates, or errors. Data scientists clean and process it to ensure it is accurate and in a usable format.

Exploratory data analysis

Data scientists explore the data to get a general understanding. They look at basic statistics and distributions to understand their structure and relationships.

Extracting information from the data

They deeply analyse data to gather valuable information from it. They use this information to solve problems, optimise existing strategies and make better decisions in the future.

Building predictive models

Data scientists develop models to predict future events or divide data into categories. They create these models using programming languages such as Python, R, C++, etc. Python is one of the most popular languages among data scientists. Approximately 69% of them use it.

Evaluating model performance

Additionally, they test the accuracy and reliability of models to ensure they work as intended.

Data visualisation and reporting

They use charts, graphs, tables, and many other visualization tools to represent the information in the best way possible. This makes it easier for others to understand their findings.

Making decisions

Based on their analysis, data scientists help companies make informed decisions. For instance, they might suggest which products to promote first or where to open a new store.

Getting feedback

After implementing decisions, they gather feedback from customers and other resources. It helps them figure out their decision’s impact and make better future decisions.

How to become a data scientist?

Here is the step-by-step guide to becoming a data scientist.

  1. Get education: Get at least a bachelor’s degree in relevant subjects. Most data scientists have degrees in computer science, data science, data analytics, statistics, mathematics, or engineering. You can get a deeper understanding of the subject through a master’s or doctorate degree.
  2. Work on your skills: Learn in-demand techniques, tools, and skills for data scientists. You should know programming languages, especially Python and R. Learn to use tools like SQL, Hadoop, and Spark that can help you perform diverse tasks.
  3. Gain experience: Internships, projects, and competitions can help you get hands-on experience. They will also give you a chance to build a portfolio and showcase your work and results to potential employers.  
  4. Connect with others: Attend conferences, seminars, and workshops to interact with your industry experts and learn from their experience. Who knows, you may find your potential employer in these gatherings.

What skills do you need to become a data scientist?

Statistical analysis: A data scientist must be proficient in statistical tests and distributions to understand data correctly. It allows them to find patterns, and make predictions and better decisions.

Programming languages: It is one of the most essential skills for data scientists. These languages help them perform many tasks, including data analysis, manipulation, and visualisation.

Machine learning: You may have to build models as a data scientist. Hence, it is essential to have familiarity with supervised and unsupervised learning, neural networks, and similar concepts.

Big data technologies: Data scientists should also have knowledge of big data platforms like Hadoop and Spark. These platforms allow them to handle and process vast datasets efficiently.

Data visualisation: You should have an understanding of tools like Tableau and PowerBI or programming libraries like Matplotlib and Seaborn. They will help you make complex insights understandable.

Soft skills: Data scientists often need to explain their findings to non-technical stakeholders. Effective Hence, effective communication is crucial. Additionally, skills like problem-solving, critical thinking, and curiosity are also essential in this profession.

How much do data scientists make in the US?

Glassdoor data reveals that the annual wage for data scientists is approximately $144,051 in the US. The estimated yearly base pay is $111,350, and additional pay is $32,700. Google, Meta, Stripe, Roku and Netflix are the top five highest-paying companies for data scientists in the US.

What does a data scientist do on a daily basis?

The day in the life of most of the data scientists starts with planning and goal setting. They meet their team members and discuss things to set these goals.

Next, they start working on the data. They spent most of their time understanding new datasets or identifying patterns. They refine existing processes and algorithms regularly to get better results.

They spend time researching new tools, methodologies and findings to increase their knowledge and stay updated in the ever-evolving industry. 

Where do data scientists work?

Data scientists can find opportunities across a wide range of sectors, such as:

Technology: Companies like Apple, Microsoft, etc. hire data scientists to improve customer recommendations, enhance user experiences, and get predictive insights.

Healthcare: Hospitals, labs, and pharmaceutical companies also need the assistance of data scientists. They help them improve treatment plans, enhance the accuracy of diagnostic tools, and optimise drug formulations.

Sports and entertainment: Data scientists can help sports teams analyse player performances, strategise game plans, and optimise training sessions. In the entertainment industry, they can help predict movie successes, understand audience preferences, and do much more.

Finance: Banks, fintech startups and investment companies also hire data scientists. They help them detect fraudulent activities, optimise trading strategies, and personalise customer experiences.

E-commerce:  Data scientists help e-commerce stores with browsing patterns, purchase histories, and cost reductions. They aim to increase their overall sales while minimizing costs.

How do I get a job as a data scientist?

Here is a simple guide to get a data scientist job: 

  1. Prepare an attractive portfolio: Create your portfolio to showcase your best projects. Mention project details, strategies you have used, and outcomes to help potential employers understand your expertise.
  2. Connect with others: Engage with industry leaders on platforms like LinkedIn. Go to seminars and workshops. It will give you opportunities to learn from others, connect with people with the same job interests, and hunt potential employers.
  3. Prepare your resume: Prepare a professional resume. Mention your educational background, key skills, and work experience. Customise it for each application by writing only the relevant skills, removing irrelevant experience, and updating that objective statement.
  4. Apply for the job: Once you have prepared a professional portfolio and resume, it is time to apply for your desired jobs. You can look for relevant jobs on online job research platforms like Indeed, Glassdoor, and ZipRecruiter.
  5. Prepare for the interview: Once you receive an interview email or call, start preparing for it. Focus on both theoretical knowledge and practical applications. Be ready to discuss your projects in detail. 

Are data scientists in demand?

As businesses recognise the power of data, the demand for skilled data scientists has increased many times. 

According to the US Bureau of Labor Statistics, the number of job opportunities for data scientists is expected to increase by 36% between 2021 and 2031. This growth rate is more than average.

Can a data scientist become a software engineer?

Yes, they can because both jobs require almost similar skills, such as coding, problem-solving and logical thinking. However, people planning to shift roles should focus on software development practices and design principles before they do so. 

The wrap-up

Data science is an in-demand job worldwide. It offers opportunities to work in various industries and make impactful contributions.

Data scientists need to have technical skills such as programming, coding, data analysis and machine learning. Some recruiters demand a degree in a relevant field, but many look for skills and expertise.

If you have a passion for data and problem-solving, a career as a data scientist can be both challenging and fulfilling for you.