Data Science Engineer Job Description, Skills, and Salary
Get to know about the duties, responsibilities, qualifications, and skills requirements of a data science engineer. You can use our job description template in this article to produce your own. We also provide you with information about the salary you can earn as a data science engineer.
Who is a Data Science Engineer?
Data science is the study of data to acquire essential business insights. It is a multidisciplinary method for analyzing massive amounts of data that integrates ideas and techniques from the domains of mathematics, statistics, artificial intelligence, and computer engineering. Data science engineers are highly specialized experts who use technology and data to address complex issues. They frequently collaborate with other engineers, scientists, and computer programmers to create new products or enhance ones that already exist.
Data science engineers are in charge of gathering, analyzing, and interpreting data to support organizational decision-making. A mathematician, scientist, statistician, or computer programmer are examples of traditional and technical occupations that are combined into the position of data science engineer. It combines the use of scientific ideas with advanced analytical methods like machine learning and predictive modelling. Data science engineers commonly have to work with large amounts of data as part of data science initiatives to develop and test hypotheses, draw conclusions, and analyze things like customer and market trends, financial risks, cybersecurity threats, stock trades, equipment maintenance requirements, and medical conditions.
Data science engineers generally mine data in firms to obtain information that may be used to forecast customer behaviour, discover new revenue opportunities, spot fraudulent activity, and fulfill other business needs. Also, they perform essential analytical work for healthcare organizations, educational institutions, governmental bodies, sports teams, and other types of organizations. Data science engineers may also be generalists with the ability to use their knowledge in a variety of industries. Regardless of their areas of expertise, they all share a passion for applying technology to address challenges in the real world.
The majority of data science engineers begin their careers in entry-level roles like data analyst or data scientist. They can advance to more senior professions like data engineer or data architecture as they gain expertise. They might be in charge of creating data models, planning and constructing data pipelines, and improving data processing systems in these tasks. Data science engineers with a management inclination could work as directors or managers in the field of data science. Senior data scientists or principal data engineers are two options for those who prefer to continue on the technical side. Those who are interested in research could work as professors or researchers in the field of data science.
Data Science Engineer Job Description
Below are the data science engineer 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.
Data science engineers have a wide range of duties and responsibilities, which can include the following:
- Create innovative algorithms for use in products or services, such as one that forecasts whether someone will buy a product after seeing it online.
- Perform statistical procedures, such as regression analysis or time series analysis, on the data to find patterns and trends.
- Discover chances for process improvement through data analysis and modelling.
- Create computer models to capture and analyze vast volumes of data, such as those utilized in artificial intelligence applications.
- Collect and get ready relevant data for analytics applications.
- Use a variety of analytics tools to find links, trends, and patterns in datasets.
- Create predictive and statistical models to test against the data sets.
- Create dashboards, reports, and data visualizations to present their findings.
- Hold meetings to discuss projects with the team.
- Gather and analyze data.
- Process automation and integration
- Look for solutions to data analytics problems.
- Create intricate mathematical models that incorporate business requirements and procedures.
- Create models for machine learning.
- Meet and communicate with IT teams, engineers, and other interested parties.
- Understandably communicate difficult concepts both verbally and visually with non-technical stakeholders.
- Create data collection techniques that get pertinent information from responders
- Create and put into use databases and data warehouses as data storage solutions.
- Visualize data to make complex information easier to understand for others.
- Create algorithms to conclude from huge data sets.
- Examine data security procedures to make sure they adhere to industry standards.
- Collaborate with data scientists and engineers to create, put into practice, and maintain scalable data solutions.
- Recognize corporate demands and specifications to create technical solutions that satisfy them.
- Create, construct, and improve data pipelines to gather, store, and process data from many sources.
- Perform data analysis to find trends, patterns, and links among data sets.
- Create and use machine learning models to generate forecasts or suggestions based on data.
- Analyze the performance and correctness of data models, and make any necessary improvements.
- Inform stakeholders and non-technical workers of data analysis conclusions.
- Create dashboards, reports, and presentations to discuss outcomes and display data.
- Keep abreast of emerging data science methodologies and tools and how to use them to address business issues.
- Work together with other teams to make sure that data is available and usable.
- Support projects involving data by assisting in the resolution of relevant problems.
- Ensure the privacy of critical information.
- Recognize the business issue, frame it as a data issue, and then use statistical and machine learning techniques to generate a solution.
- Build predictive models, run analyses, and produce insights that may be used in decision-making using large-scale datasets.
- Use graphics and other methods as necessary to clearly and succinctly convey findings to non-technical audiences.
- Join forces with engineering teams to put data-driven solutions into practice.
- Keep abreast of recent advancements in the industry and use them to address challenges
- Find opportunities to obtain fresh data that can be valuable for resolving issues in the corporate world.
- Investigate data to look for trends and connections.
- Create algorithms and forecasting models
- Analyze model performance and make improvements.
- Present analysis findings to relevant parties.
- Bachelor’s degree in computer science, data science, engineering, or related discipline.
- At least 3 years of experience working in data science.
- Experience with Hadoop, Spark, Hive, and other data platforms.
- Experience with NoSQL databases like MongoDB, Cassandra, and HBase
- Advanced program in machine learning and programming.
- Data visualization tools expertise.
- Proficiency in data querying languages, and statistical or mathematical software.
- Thorough knowledge of algorithms writing and then being able to apply the algorithm.
- In-depth understanding of statistics, multivariable calculus, and linear algebra.
- Experience with cloud computing platforms, such as Amazon Web Services (AWS) and Microsoft Azure
- Python, and R proficiency.
- Experience with statistical modelling is required.
- Excellent analytical and problem-solving skills
- Outstanding communication and presentation skills
- Programming languages skills: Data science engineers are required to be skilled in several programming languages. Python, Java, and SQL are just a few of the computer languages that data science engineers need to be proficient in. These languages are used to develop applications that analyze data and produce visualizations. To write programs that operate effectively and efficiently, data science engineers must be knowledgeable in these languages.
- Organizational skills: The capacity to keep track of files, records, and other resources so you can locate them when needed is an organizational skill. The huge amounts of data that data science engineers frequently work with require organizations to be efficiently analyzed. Additionally, when developing models or prototypes for their tasks, they apply organizing skills. By doing this, the engineer can be sure that all the necessary parts are there and can more readily troubleshoot any problems.
- Tableau: Data science engineers study and understand data using Tableau, an application that enables users to generate visual representations of data. Knowing how to use this tool will enable you to visualize complicated data sets and make better judgments in the course of your projects. To progress in their jobs, data science engineers generally need to become proficient with new software, such as Tableau.
- Leadership skills: Because they regularly manage groups of other data scientists, data science engineers need strong leadership skills. They must be able to assign duties, offer direction, and inspire their team members to accomplish projects. When working on projects with clients, data science engineers must also have leadership skills because they may need to haggle over deadlines, costs, and deliverables.
- Creativity: Data science engineers usually employ creativity to address challenging issues. It would be essential for them to create new techniques for data analysis or models that can forecast outcomes depending on input variables. Since data science is a recent subject, many of the tools and methods they employ are probably original. When coming up with solutions, they must think creatively.
- Machine learning skills: The ability to use data science to develop algorithms that can learn and get better over time is known as machine learning skills. It is essential for data science engineers to possess this skill so they can write programs that get better over time.
- Data visualization skills: The ability and process of making graphs and charts from data are known as data visualization skills. To convey data to clients or other stakeholders, data science engineers often use data visualization. Data science engineers should be proficient in data visualization.
- Problem-solving skills: Data science engineers must have problem-solving skills to detect and resolve problems in their job. When they come across technological difficulties that prohibit them from finishing work, like software glitches or hardware issues, they apply problem-solving skills. Data science engineers also use problem-solving skills to find solutions to non-technical issues, such as figuring out how to increase an algorithm’s effectiveness or lower production costs.
- Algorithms: The steps taken to solve an issue are called algorithms. Algorithms are used by data science engineers to build models and solve data challenges. Data science engineers must know how to design algorithms and how to test them to make sure they function.
- Communication skills: Data science engineers regularly communicate with clients, stakeholders, and other team members. For non-technical persons to understand difficult technical information, data science engineers may also need to explain it. You would make others feel understood and deliver information clearly by using effective communication skills.
How to Become a Data Science Engineer
Step 1. Earn a degree in a related field
The minimal educational requirement for careers in data science engineering is a bachelor’s degree in computer science, mathematics, engineering, statistics, or a related discipline. Candidates with graduate degrees in data science, analytics, or a related discipline are commonly preferred by employers. Algorithms, programming, data mining, and database management are typically included in data science undergraduate degrees. Advanced topics including machine learning, information retrieval, natural language processing, and visual analytics are emphasized in graduate programs. Some universities offer specialized degrees in data science that integrate academic study with the real-world experience gained through capstone projects or internships.
Step 2. Acquire work experience
Data science engineers must be well-versed in statistics and data analysis. Before deciding to pursue a career in data science engineering, consider working as an analyst or statistician. In these sectors, a bachelor’s degree is frequently required for entry-level work, although experience is typically sufficient for higher-level positions. You can hone your abilities in data gathering, organization, and analysis by working as a statistician. You might also work on projects involving data mining, which is sifting through a lot of data to find usable information. Your understanding of how data science engineers use the findings of their studies to make decisions about business operations will improve as a consequence of this encounter. After being employed, data science engineers often receive on-the-job training. This training could last for a few months and might cover information on the particular systems and practices used by the organization.
Step 3. Become a member of a professional association
The Association for Computing Machinery (ACM) is a recommended association for data science engineers. The ACM is an international organization that offers educational and networking opportunities to computer professionals. The ACM Digital Library provides free access to articles, videos, and other materials on data science engineering. As a student, you can join the ACM or start with an associate membership after graduation. However, as your career advances, you can require to become a full-fledged professional member of the association.
Step 4. Acquire certification
Data science engineers can acquire some certifications that prove their expertise in different types of data, systems, and algorithms.
Where to Work as a Data Science Engineer
Data science engineers work in a variety of places such as corporate offices, government agencies, healthcare, finance, manufacturing, retail, and educational institutions. They may work in a laboratory. Data science engineers typically work full time, and some may work more than the normal work hours per week to meet deadlines or to accomplish tasks. They may be required to travel often either to attend conferences, meet with clients, or carry out field research. Data science engineers need to be able to work independently and as part of a team.
Data Science Engineer Salary Scale
The salary scale of data science engineers can vary widely depending on many important factors such as level of education, certifications, additional skills, and years of experience in the profession. The average salary of a Data Science Engineer in the United States is $150,688, but the salary range typically falls between $135,796 and $166,801 per year. The average salary of a Data Science Engineer salary in the UK is £39,971 annually, ranging from £22,490 to £95,000. The salary scale of a Data Science Engineer in Canada is from $95,899 to $120,503, with an average of about $105,000 per year. In Nigeria, these Engineers earn about 6,200,000 NGN per year. The sum of 3,540,000 NGN to 9,110,000 NGN is the salary scale per year.