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 data scientist 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 are big data wranglers who gather and analyze large amounts of unstructured and structured data. They combine computer science, mathematics, and statistics. These professionals process, analyze, and model data, then interpret the results to develop actionable plans for companies or other organizations.
Data scientists are experts in technology and social sciences who use their knowledge to identify trends and manage data. To solve business problems, they use their industry knowledge, context understanding, and skepticism about existing assumptions.
Data scientists are responsible for making sense of unstructured and unorganized data from various sources, including smart devices, social media feeds, and emails that don’t fit neatly into a database.
The role of a data scientist involves the combination of many traditional technical roles such as scientist, mathematician, and statistician. This job requires advanced analytics technologies such as machine learning and predictive modeling.
Data scientists need large quantities of data to make hypotheses, draw inferences and analyze market and customer trends. The basic responsibilities of a data scientist include collecting and analyzing data and using different types of analytics and reporting tools to identify patterns, trends, and relationships within data sets.
Data scientists in business often work in teams to extract big data from customers to identify revenue opportunities and predict their behavior. Data scientists in many companies are responsible for setting the best practices for data collection, analysis, and interpretation.
Data Scientist Job Description
Below are the data scientist job description examples you can use to develop your resume or write a data scientist job description for your employee. Employers can also use it to sieve out job seekers when choosing candidates for interviews.
- Identifying and integrating new datasets through product capabilities.
- Working closely with the engineering team to strategize for and execute the development of data products
- Performing analytical experiments methodically to solve different problems and have an impact on various industries and domains.
- Recognizing relevant data sources and sets for clients’ business needs and gathering large structured and unstructured variables and datasets.
- Developing and using algorithms and models to mine large data stores.
- Performing data analysis to improve models.
- Cleaning and validating information and data to ensure uniformity and accuracy.
- Analyzing data to identify trends and patterns and interpreting data with a clear goal in mind
- Collaborating with software developers and engineers who are skilled in machine learning to implement analytical models into production.
- Communicating analytical solutions to implement improvements to operational systems as necessary
- Combining the knowledge of mathematics, computer science, applications, modelling, statistics, and analytics to solve problems
- Extracting different and multiple sources of data
- Analyzing data from many angles to find trends and identify problems or opportunities.
- Communicating key information to IT and business leaders
- Using machine learning techniques to select features and optimize classifiers.
- Using data mining state-of-the-art methods for solving problems
- Extending company data to third-party sources of information
- Improving data collection to include relevant information for building analytic systems,
- Cleansing and verifying data
- Analyzing the data and then presenting the results in a clear way
- Transferring data to a new format to make it more suitable for analysis
- Collecting data using new experimental frameworks
- Automating data collection by building tools
- Creating reports and presentations for business purposes
- Asking the right questions to start the discovery process
- Cleansing and processing data
- Integrating and storing different forms of data
- Selecting one or more models and algorithms for data integration
- Utilizing data science techniques such as statistical modeling, machine learning, and artificial intelligence
Qualifications
You must have the passion and knowledge to succeed as a data scientist. This requires the right education, skills, and other qualifications. These are the requirements to become a data scientist.
Education
A minimum of an undergraduate degree in data science is required to become a data scientist. A degree in computer science will teach you about programming languages, computing systems, and database management. Computer engineering focuses more on system engineering, cybersecurity, and overall network architecture.
You may be able to bypass a related degree to pursue a career in data science if you learn the technical skills necessary for the job through online classes. A master’s degree is not required, but some jobs may require an advanced degree in qualitative disciplines. Data scientists are most likely to have a master’s or Ph.D. so it is worth considering if you want to be competitive with other applicants for jobs.
You can also pursue a specialization in data science while you are completing your education. This could include artificial intelligence, research and database management, or machine learning. This will not only increase your data science knowledge but also allow you to earn more if you are hired by managers who need data scientists with specialized skills.
Training
Before they can start their full-time jobs, data scientists spend time training in the industry they choose. These training periods allow data scientists to adapt to new environments and improve their academic skills in a variety of industries.
Certification
Data scientists do not need to have certifications. They can seek out one of the optional certifications that will give them an advantage over other candidates and make them more attractive to potential employers.
Essential Skills
- Cloud computing
Data scientists need to be able to use cloud computing because it allows them to store, retrieve and share data. They need to be able to navigate the cloud computing environment shared by many companies. Data scientists are responsible for retrieving and analyzing the data from the cloud, even though businesses may not realize it.
- Probability and statistics
Data science, at its core, is the use of algorithms, systems, and processes to gain more information about data. This allows them to make informed decisions, gain insight, and extract important information from a database. Data scientists often have to predict the performance of different data. Data scientists who can use statistical methods can help companies make these predictions and analyze their data.
Data scientists are skilled in probability and statistics and can forecast trends and create forecasts, find anomalies in the data set, establish relationships between points in the dataset, and learn more about the data they work with.
- Advanced mathematics
Data scientists can use multivariate and linear calculus to accomplish their work. Calculus is the main tool they use to create a machine-learning model. However, they can also use cost function, derivatives, plotting, gradients, and algebra. They may also use advanced mathematics tools to aid them in calculations. It is therefore important to be familiar with the principles of calculus, algebra, and how they affect their reports.
- Machine learning
Machine learning is the use of statistics to identify patterns in data. Machine learning is artificial intelligence used by data scientists to support their assumptions regarding a set of data points. Machine learning can help relieve data scientists of some of their responsibilities and prevent human error. Machine learning is even more useful when there are large amounts of data available to data scientists. It can create workable algorithms and models that can be used by others to process the data in real-time.
- Data visualization tools
Data scientists use data visualization software to convert data and information into visuals such as graphs and images. This may be done to better understand their data reports and to provide information to company stakeholders who have requested the data. Data visualization tools make it easier for data scientists and data analysts to identify patterns, trends, and other outlying points in data sets.
- Query Language
A query language is a language used by data scientists to inquire about the databases and information that they have. Structured query language (SQL) allows data scientists to quickly retrieve data to help them solve specific problems or find solutions to certain questions.
- Database management
Data scientists must be able to manage data because they work with it. This involves being able to retrieve the correct data efficiently without affecting other parts of the database. Data scientists must be familiar with a variety of database management tools and systems so that they can collaborate with other companies to update, store and retrieve data from their systems.
- Visualizations
Data scientists must not only be proficient in the tools used to translate data into visuals but also be able to interpret those visualizations to better understand the data. Visualizations can include line plots, pie charts, 3-D plots, or bar charts. Data scientists can create visualizations based on the variables in the data. Data scientists can quickly find the answers to their questions or pinpoint areas that need improvement by using visualizations.
- Python programming
Python is an open-source programming language that data scientists can use to manipulate and understand data. Python works well with machine learning, artificial intelligence tools, and other programming languages to present data in an easy-to-understand format that data scientists can understand.
- Microsoft Excel
Although there are many more sophisticated ways to create data listings, Microsoft Excel can be used by data scientists. Excel allows data scientists to create databases with custom labels and sort and filter data. You can also create tables in Excel that can be used for calculation functions.
- Programming in R
This is the type of math that data scientists use in their work. R can also be used to integrate with other systems to give a better understanding of data. Data scientists can use R to better analyze data and identify trends. They can also create visualizations that include data points or groups. And they can predict the future with R. R is a great tool for data scientists, both new and experienced.
Data wrangling
Data wrangling is another skill many data scientists possess. This involves cleaning and manipulating raw data to remove outliers, change null values, and convert the data into an easy-to-use format. Data wrangling allows data scientists to make faster decisions, especially when working with large quantities of data.
How to Become a Data Scientist
- Earn a bachelor’s degree
Data scientists typically complete four years of an undergraduate degree in IT, math, or computer science. Many undergraduates have worked at least one internship before being hired in an entry-level position. This is why it’s important to network with data scientists early in your college career to find the best route. Interviews can help you get a better understanding of the daily schedule and hiring requirements for data scientists.
- Get certified
Certificates that show you have mastered a skill or extended your training can be an added benefit. You can get certifications such as the Microsoft MCSE Data Management, Analytics Certification, Data Science, and Advanced Analytics Associate (DECA DS) Certification. These certifications can be a benefit when you apply for entry-level positions or graduate programs.
- Gain work experience
Data scientists are in high demand. Finding entry-level positions in data science can lead to junior data scientists or junior analysts. Companies may offer certifications and training on the job that could lead to promotion while you are working as an entry-level employee.
- Your portfolio should be built
A portfolio of examples of projects that have had an impact on the company is a key asset for a data scientist. Visual presentations of projects from beginning to end give you an extra advantage over candidates with similar training.
- Pursue a master’s degree
Data scientists should earn a master’s in engineering, computer science, or business administration to be able to lead. A master’s degree may give you an edge over other candidates for data scientist jobs. To find out which areas you should have experience in, check the requirements of each program.
- Pursue a doctorate
Data scientists at the top may hold a Ph.D. in mathematics, or data science. A doctorate could help you get a job as a teacher or in an executive position within an industry-specific organization.
Where to Work
Data scientists are often employed by the government, computer system design, or related services. They also work in the research and development department for colleges and universities, as well as for software publishers.
Data Scientist Salary Scale
Data scientists can work full-time, but some may also be part-time or contract employees. Data scientists’ salaries vary depending on their education level and work experience, as well as the industry and company size of the employer.
The average salary of a data scientist in the U.S. is $123,263 per annum.