Ultimate Guide to Data Scientist: Job Description, Responsibilities, Skills, Salary, and Certifications

Introduction
Organizations today strongly adhere to data-driven analytics and interpretations to frame the proactive strategies and prognostication of company growth. Data science emerged as a rapidly evolving industry that handled all the unfolded data insights for sophisticated data extraction in every business. Data science is marked by its vital existence in industries like healthcare, information technology, education, finance, e-commerce, government, and retail – to name a few. As the demand for data science in businesses is escalating, there is no doubt about the scope of data scientists in the career market, but it can also be one of the most difficult areas to break into. There are several skills and certifications required to succeed in this industry, so let’s look at what you need to know to get started in data science today.

What is data science?

Data science integrates mathematics, statistics, computer science, computer programming, sophisticated analytics, artificial intelligence (AI), and machine learning with unique subject matter knowledge to mine the data and present it well to help organizations in decision-making. Identifying a large amount of data and shaping the successful data science lifecycle are significant concepts involved in data science and are the major roles of a skilled data scientist.

Data scientist job description

In general, a data scientist job description involves performing comprehensive checks on data and managing a giant database to extract and interpret that data to help organizations take proactive measures. Data scientists leverage mathematical skills and interpretation skills in collecting, processing, analyzing, and extrapolating huge amounts of structured and unstructured data to derive data-driven solutions for the company. Hence it is no surprise that Data scientist jobs are known as one of the high-paying IT jobs in the current century as per LinkedIn search insights in recent years.

Data scientist responsibilities

If you want to work as a data scientist, you first need to grasp the data scientist responsibilities of the profession. Let’s look at major data scientist responsibilities in current professionals to get an idea of how you might use your skills and talents in this career path:

• Perform calculations and outline examinations to consolidate, merge, extract, and gather data to deliver fitted reports to partners, clients, or the wider business audience.
• Keep up with clear and intelligible verbal and written correspondence to comprehend required data and produce reports.
• Use AI tools, machine learning tools, and measurable methods to frame solutions for the issues.
• Search for possibilities to utilize data reports/datasets/code/models across different capabilities in the association (for instance, in the HR, sales, and marketing departments).
• Work intimately with your business to recognize issues and use the information to propose answers for successful decision-making.
• Test all data mining models to choose suitable ones to integrate into any project.
• Make clear reports that recount how customers or clients work with the business.
• Stay up-to-date with the most recent innovations, strategies, and techniques.
• Remain inquisitive and excited about utilizing calculations to mitigate problems and help others observe the advantages of your work.
• Analyze big data structures and identify patterns to develop conclusions in presentable formats.
• Present solutions and create strategies for all business challenges arising in the departments based on strong data extrapolation.

Data scientist skills and requirements

A professional data scientist needs to possess advanced technical skills such as mathematical modeling capabilities, programming abilities, database design skills, communication skills, and diagnostic reasoning abilities. They typically use tools like statistical analysis, predictive modeling, machine learning, and other statistical methods to help a company solve its problems. The following are the essential data scientist skills and qualifications to excel in every industry.

Technical skills:

Basic qualification for a data scientist: The professional should be undergraduate or postgraduate in computer science, mathematics, and/or science-based subjects.

Desirable knowledge: The general and in-depth knowledge in disciplines like physics, statistics, finance, economy, management, information technology, and relevant subjects.

Programming skills: Strong knowledge of programming languages and database interrogation tools like R, Python, SQL, Hive, Pig, Scala, Java, Perl, C++, Julia, MATLAB, TensorFlow, and any other computer science skills are required to outshine the data scientist from packs.

Mathematical and statistical skills: The ability to solve complicated mathematical functions and find ways to incorporate results in pattern analysis is a significant skill for a data expert. Other important mathematical and statistical skills for data scientists include calculating tremendous data based on multivariable calculus and linear algebra. The adept in differentiation, derivatives, cost functions, functions (step function, Sigmoid function, etc.), maxima and minima, scalar functions and vector functions, and a lot more add the value of skillsets.

Expertise in data wrangling, database management, data visualization, and data interpretation.
Knowledge of Microsoft products for the presentation of data to stakeholders.

Non-technical skills:

• Fluent communication skills to disseminate the strategies to the team and present the summary to the business audience. Communication can have a big impact on your project outcome and lead to a successful deployment.
• Exceptional teamwork skills, collaborating with engineering and development teams to share ideas and reach data-driven conclusions.
• Great analytical, management, and problem-solving skills.
• Effective listening skills to understand the key points of clients and stakeholders to deliver the structured data presentation.
• Attention to detail skills to spot errors and rectify them in the correct stages to deliver faultless reports.

8 best data scientist certifications
Here are the top data scientist certifications that can bring an instant boost to your career:

• SAS certifications (SAS advanced analytics professional certification & SAS certified data curation professional)
• Certified data professional (CDP)
• Microsoft Azure certification (Microsoft certified Azure data scientist associate)
• IBM data science professional certificate
• Google professional data engineer certification
• Harvardx’s data science professional certificate
• Cloudera certified associate (CCA) data analyst
• Cloudera certified professional (CCP) data engineer

Data scientist salary in the USA

How much do data scientists make? This is a trending question on Google, demonstrating the interest of aspirants in evaluating the payscale of a data scientist career so that they can decide whether this job is worth pursuing. The data scientist salary varies depending on their title and experience. Based on the salary insight studies from Glassdoor (link provided for transparency), the average entry-level salary of a data scientist is $93,167 per year. As you become more experienced, your salaries will increase from $156,000 annually to higher based on your skills and qualifications.