Data Scientist Job Description

Data Scientist Job Description, Skills and Salary

Get to know about the duties, responsibilities, qualifications, and skills requirements of a data scientist. Feel free to use our job description template to produce your own. We also provide you with information about the salary you can earn as a data scientist.

 

Who is a Data Scientist?

Data scientists’ work combines the fields of statistics, computer science, and math. They process, analyze, and model data, then use their findings to formulate practical plans for businesses as well as other organizations.

Data scientists are experts in analysis who make use of their knowledge in both social and technical sciences to identify patterns and manage data. They employ industry knowledge, context-based understanding, and skepticism of current assumptions to find solutions to business problems.

Data scientists’ work usually involves analyzing messy and unstructured data from sources like smartphones as well as feeds from social media and emails that do not integrate into databases.

Data scientists study large data sets and create applications to parse the data and find patterns. They identify what they require to know from the data and develop algorithms to can organize and manage the data. They work with computer researchers and IT professionals to gather massive amounts of data to solve difficult issues and discover business trends. They usually use computer programming languages such as Ruby, Python, and SQL.

The role of a data scientist is an extension of a variety of traditional technical positions, such as statistician, scientist, mathematician, and computer expert. The job demands the use of sophisticated technology in analytics, such as predictive modeling and machine learning.

Data scientists require massive amounts of data to formulate hypotheses, draw inferences, and study market and customer trends. The primary responsibilities of a data scientist are gathering and analyzing data using different types of tools for reporting and analytics to identify patterns, trends, and connections within data sets.

In the business world, data scientists are typically employed as part of teams that mine huge data for businesses which can help forecast customer behavior and find potential revenue streams. In many companies, data scientists are also accountable for establishing guidelines for gathering data by using analysis tools.

The need for skilled data scientists has risen substantially over the past few years as companies seek to extract useful information from massive data.

 

Data Scientist Job Description

Below are the data scientist job description examples you can use to develop your resume or write a job description for your employee. Employers can also use it to sieve out job seekers when choosing candidates for interviews.

The following are what to expect as a Data Scientist job description. However, you should note that Data Scientist job description listed here is not exhaustive.

  • Finding the most important analytics-related issues that companies have to deal with and assisting in solving them.
  • Recognizing accurate and incorrect data sets
  • Extracting massive data sets of unstructured and structured data from various sources
  • Applying and developing algorithms to extract big data
  • Utilizing tools for reporting and providing analytics to determine relationships, trends, and patterns.
  • Completing and cleaning data to make ensure accuracy and completeness
  • Getting together with the company’s stakeholders as well as management and IT departments to share findings via visualizations and reports
  • Recommending changes to current methods and strategies of the company.
  • Finding the appropriate questions to start the process of discovering
  • Integrating and keeping track of information
  • Investigating data along with exploratory data analyses
  • Selecting one or more possible models and algorithms
  • Utilizing data science techniques like statistical modeling, machine learning, and artificial intelligence
  • Enhancing and measuring results
  • Finalizing results for stakeholders
  • Providing adjustments based on feedback

 

Qualifications

If you’re considering becoming a data scientist, it is essential to be able to comprehend the subject and determination to succeed within this area. This requires having the right training, qualifications, and certifications. Here are the basic prerequisites for becoming a data scientist.

 

Education

To be a data scientist, you must have a minimum of an associate’s degree or bachelor’s degree in a related discipline like statistics or computer science, engineering, or information systems. A master’s degree in computer science can also enlighten students about computing systems, programming languages, math, and the management of databases. Computer engineering is focused on cybersecurity, systems engineering, and the overall architecture of networks.

If you are planning to switch careers to Data Science, you might be able to skip an equivalent degree by acquiring the technical abilities necessary for this job by taking online courses. In addition, although having a master’s degree isn’t always necessary for this field, however, certain jobs might require higher qualifications. It’s worth noting that many data scientists possess an advanced degree as well as a Ph.D. So, having a higher degree in this field could make you more competitive against other job candidates.

As you continue to work to improve your knowledge of data science, you can also think about an area of specialization, such as research, artificial intelligence and database management, and machine learning. This will not only increase the depth of your knowledge in one specific area of data science but can also boost the amount you earn if hiring managers are looking to hire data scientists with specialization.

 

Training

Data scientists usually master the fundamental knowledge and skills they require to perform their job while earning degrees and certificates. However, they engage in training programs to be familiar with the workflows of their employers, procedures, and policies. The duration of the training program is dependent on the employer’s requirements and is generally paid. It is also possible to pursue internships while at university or after graduation for additional education.

 

Certifications

A lot of companies require that data science professionals possess an official certification to prove their expertise in specific areas. The most commonly used certifications in data science are:

 

  • Azure Data Scientist Associate

This Microsoft certification proves that applicants can use Microsoft Azure’s machine-learning tools to build models based on data that can help solve business challenges. Candidates must successfully pass an exam to get this credential.

 

  • Certified Data Architect

This IBM certification shows that the candidates can translate business requirements into solutions based on data using IBM applications. Candidates must know Hadoop as well as BigSQL programming languages, and successfully pass an exam to earn this certificate.

 

  • Certified Data Scientist

The Certified Data Scientist SAS certification signifies that data scientists can make use of SAS technologies to alter large data. To obtain this certificate applicants must possess at least SAS Certified Big Data Professional and the SAS Certified Advanced Analytics Professional certification, which requires taking five tests.

 

  • Principal Data Scientist or Senior Data Scientist

The certifications demonstrate that the candidates have extensive experience in data engineering and analytics. It requires at least six years of professional experience and a 100-minute exam. The principal certification on the other hand requires 10 years of work experience and 100 minutes of the exam.

 

Essential Skills

Any successful data scientist need to have a fundamental knowledge of math and statistics. Every business, particularly those that are based on data, expects a data scientist to understand the various approaches to statistics which include distributors, estimators with maximum likelihood, and statistical tests to make decisions and recommendations. Calculus along with linear algebra is essential because they’re both linked in the machine learning algorithm.

 

  • Analytics and Modelling

Data can only be as effective as the people who are performing the modeling and analytics on it, which is why a proficient data scientist is required to be proficient in this field. With good critical thinking as well as communication skills, the data scientist is expected to be competent in analyzing data, running tests, and building models that can provide new information and anticipate possible outcomes.

Data science may require a portion of data processing processes that are dedicated to analyzing raw data, as well as software-interpreted data. This is why data scientists are typically able to demonstrate excellent analytical skills. For example, data science could necessitate the analysis of the right information to be used in medical or financial reporting.

 

  • Machine Learning Methods

Although having a deep understanding of this subject isn’t essential, a certain degree of experience is required. Logistic regression, decision trees, and more are important elements that machine learning can provide and prospective employers will be seeking these abilities.

 

  • Programming

To move from the theory to creating practical applications, the data scientist needs strong programming abilities. Many companies will require you to be proficient in Python and different programming languages. Basic syntax, object-oriented programming, flows, statements for flow control along with documentation and libraries all belong to this umbrella.

 

  • Data Visualization

Data visualization is a crucial element of becoming a data scientist because you have to be able to convey important messages and also get acceptance for your proposed solutions. Learning how to dissect complex data into digestible pieces and employing various visual aids (graphs, charts, and many more) is a key skill every data scientist will need to master to make progress in their career.

 

  • Intellectual Curiosity

The core of the job of a data scientist is a keen desire to find solutions to problems, particularly those which require you to think outside the box. A good data scientist is fueled by an interest in learning more about what data can tell them, and how it can be utilized to improve the overall performance of their company.

 

  • Communication

A good data scientist needs to have an excellent ability to communicate. It doesn’t matter if it’s distributing to your team the steps you’re planning to take to go from A to B using the data or the approach you take in providing presentations to business leaders and management, communicating effectively can be crucial in the outcome of the project.

  • Business Acumen

For a data scientist to effectively use data in a manner that’s beneficial for their employer, an amount of business acumen is required. You must be able to comprehend the primary goals and objectives of the company, and how they impact your work. Additionally, you need to develop solutions that can accomplish these objectives in a manner that is efficient, cost-effective, and ensures wide acceptance.

 

  • Technical expertise

Technical expertise is among the essential skills employers will require when hiring a data scientist. Statistics and data platforms for visualizing data processing software, as well as closed-source and open-source software are all common technical skills that data scientists have.

 

  • Teamwork skills

This ability is vital to the position of a data scientist. Most often, data scientists collaborate with or even manage an entire team comprising mathematicians, developers, researchers, and others who are part of a project. Furthermore, data science could involve collaboration with different departments within an organization to facilitate communication and productivity.

 

  • Quantitative skills

Data scientists could also possess impressive reasoning and quantitative capabilities. For example, a data scientist employed by an enterprise that produces software could use their mathematical skills to study raw data, identify patterns, analyze the data, and then organize the process into quantifiable outcomes.

 

How to Become a Data Scientist

  1. Get a degree

Employers typically require applicants to possess at least one master’s degree, and some might even require having a doctorate. Find job openings for data scientists within your area. Find out the degree of education necessary to be qualified.

 

  1. Gain experience

Data scientists are generally required to have at least seven years of experience to be eligible for a job. If you’re still in school, search for internships where you have the opportunity to observe an experienced data scientist. If you’re just graduating, seek out opportunities in information technology in which you can learn the skills in programming and computers necessary for the job.

 

3.:You can get certified

Although not required, certifications increase your chances of being considered an applicant, since certification can prove your skills. There are many certifications for vendors and non-vendors in the field of information technology.

 

  1. Update your resume

Once you’ve acquired the necessary knowledge, qualifications, and some certificates, make sure you update your resume. Include the highest degree of education in the workplace. In each job, the entry makes sure you highlight the skills that are transferrable for the job of the data scientist.

 

  1. Apply for jobs

Search for open positions as a data scientist within your region and pinpoint the jobs that you are most competent for based on your education or experience, as well as your skills. Make an application using your most recent resume and cover letter you’ve tailored to the job for which you’re applying.

 

Where to Work

Data scientists typically work for authorities, government agencies, system design, or other related services as researchers and developers. They also work in universities and colleges.

 

Data Scientist Salary Scale

A large majority of data scientists work full time although some are employed part-time or as contract employees. Data scientists’ salaries vary by the amount of education, relevant work experience, and also the industry of the employer, its size, and geographic area.

The average salary of a data scientist in the U.S is $123,263 per year.

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