What Do Data Scientists Do?

 

Data Scientist: Roles, Responsibilities, And Career-Graph

The proliferating amount of Big Data we leave behind is astounding. And there is a group of sleuths - or data scientists – who filter these enormous chunks of information to deliver strategic, coherent insights. Industries are hiring skilled data scientists to research and analyze big data and arrive at fruitful insights to unlock better opportunities and strategies for the business. Data science, spreading its wings from technology and retail, has exponentially expanded to other fields such as medicine, finance, and investment.

Professionals working as data scientists get to solve some of the most exciting questions of the real world. Plus, they are getting paid a handsome amount to do their job. These factors motivate the budding professionals and freshers to enroll in a data science bootcamp and graduate as a data scientist. 

In this write-up, you will learn more about data scientists and their roles and responsibilities.

What Is A Data Scientist?

The HBR rightly dubbed data scientists as the sexiest job of the 21st century. A data scientist analyzes and extrapolates Big Data with the help of computation, mathematics, and statistics to derive meaningful insights. It is an exploratory field, where a data scientist spends a lot of time collecting, organizing, and examining the data from multiple sources.

The technology and retail sectors were the primary employers of data scientists. But with significant mainstream industries taking a data-driven business outlook, these professionals have deviated into multiple sectors such as medicine, finance, healthcare, insurance, banking, and investment. These professionals are highly sought-after and handsomely paid.

Responsibilities Of A Data Scientist

The responsibilities of a data scientist are mostly industry-specific. However, these professionals are generally expected to perform the following duties and tasks.

1. Capture Data: In this step, a data scientist collects the data on the topic of discussion, ranging from pain points of the company to improve the productivity of the resources
2. Maintain Data: The data scientist defines, sorts, and catalogs the data sets to derive relevant and valuable information necessary for analysis. 
3. Process: Here data scientist cleans, processes, and filters the data and prepares it for analysis.
4. Analyze: This step mainly comprises creating and applying algorithms to identify latent patterns and trends.
5. Communicate: A data scientist uses multiple visualization tools for presenting the data in a visual format for non-technical members of the organization can effortlessly comprehend the data.  
6. These phases are iterative since they can loop back to one or more phases before.  

Common Tools For Data Science

The best data science bootcamp in the Bay Area points out data science is an interdisciplinary field that combines statistics, computation, programming, and business domain knowledge to dig out the answers. They rely on several specialized tools for cleaning, analyzing, and visualizing the data.

Python is the go-to tool for most data scientists. The language helps in running complex statistical functions or processing natural language. Machine learning tools help the data scientists to teach the computer system to find output without elaborate programming. For this job, TensorFlow and Accord.Net are primarily used.

Data visualization is an integral part of a data scientist’s job description. The data visualization tools convert the large number and texts into visual representations – charts, figures, and diagrams – for the comprehension of non-technical readers. For this step, data scientists use Tableau or Bokeh.

Career Roadmap Of Data Scientist

The career roadmap of a data scientist is mainly divided into three general levels with internal bifurcations, depending on their skills, responsibilities, and total compensation. Often senior data scientist roles deviate to managerial tasks where they have to scale strategies, overlook and manage other team members, and aid in running the organization together.

The primary three career roadmaps of a data scientist are:

• Intern & Entry-Level Data Scientist

Entry-level data scientists can earn up to $100,000 annually, but the total compensation can be pretty high in FAANG companies. These professionals are in the early stages of their careers and are mostly responsible for straightforward and clear objective goals. The job roles include building scripts or prototyping projects with data visualizations.

• Mid-Level Data Scientist

An entry-level data scientist transgresses to mid-level roles after 2 to 3 years of experience. At this level, a professional is considered an advanced individual contributor who can take up larger project scopes and more ambiguous business problems. Most mid-level data scientists earn anywhere from $120k to $180k. 

• Senior Data Scientist

4 to 5 years of experience in the field is essential for a mid-level data scientist to get promoted to a senior level. They analyze the technical problems and work them through business and technical architecture at this position. Other responsibilities such as training and mentoring junior data scientists are a part of the job description. 

Is Data Science Right For You?

The high-demand roles of a data scientist are critical for the success of a business. Each day a company collects a massive amount of data that stays neglected for most of its existence. A data scientist can manage, analyze, and work through these unfiltered data to provide extensive insight into a given problem.

These professionals solve interesting industry-related problems and get paid handsomely for the same. These motivators encourage budding professionals and freshers to enroll in a data science bootcamp and become certified data scientists.

Data science is an exciting field, but is it right for you? If you like solving problems and have a knack for numbers, then this is the field for you. SynergisticIT runs an online data science and coding bootcamp that is helping professionals in landing the best jobs in the industry. This career-oriented program includes interactive lessons on necessary skill sets like computation and statistics, programming and coding in Python, and other critical tools to make them stand out from the crowd.