Data science is all about using data to solve human problems whether in business or on a global scale. It’s about identifying patterns based on previous activities and smarter making decisions and predictions for the present and the future. It’s like a career in prophecy, using carefully organized information from the past as your source of divine knowledge. Almost every business needs the service of a prophet… oh I mean data scientist.
Data science has been described as one of the best jobs of the 21st century. In 2019, a global job-search website Glassdoor ranked Data science as the Best Job in the US. With this high demand placed on data experts, anyone with sufficient knowledge in any of the related fields is almost guaranteed job opportunities across the globe.
Once you have mastered data science skills like python, R, SQL, and other technical skills, you can readily find job opportunity. Let’s look at the most sought-after and high paying jobs in data science careers. The salaries generally depend on factors like level of experience, educational background, size of the company, and location.
Potential Earning: $78,752-$88,752
Data Analyst is the most basic entrance level opportunity in the field of Data Science. The job of a Data Analyst is to use the data of companies and industries to answer business questions that will position them for maximum productivity and effectiveness. Data analysts will have the opportunity of working with different teams within an organization. As a Data Analyst, you will be saddled with the responsibility of coming up with insights from the given records instead of just predicting future occurrences using machine learning.
Generally, all Data Analyst positions will require:
Choosing a career path in Data Analysis opens you to many opportunities. As long as you keep developing your skills in data science and machine learning, you stand a chance of being hired as a senior data analyst by top companies with data teams.
Potential Earning: $138,173
A data scientist does so many things like a data analyst but focuses more on building models for machine learning for making precise predictions about occurrences of the future using past data records. As a data scientist, you have the autonomy and freedom of pursuing your experiments and ideas to find amazing trends and patterns in the records and data that the organization may not have considered.
A data scientist assesses the effect changes in marketing strategy have on the overall performance of the company. The job of a Data scientist requires a lot of data analysis and building machine learning models for making future predictions based on records. In addition to all the skills required from a data analyst, a data scientist require a strong understanding of both unsupervised and supervised methods of machine learning; Solid understanding of statistics and statistical model evaluation and advanced data science programming.
Potential earnings: $142,653
A data engineer is responsible for managing organizational data infrastructure. The job position requires the use of so many statistical analyses, programming skills, and software development. At an organization with data teams, a data engineer will be responsible for constructing data pipelines targeted at attracting more sales, and revenue data. Similarly, they are responsible for maintaining and building the required structures for storing and accessing past data records.
The required skills for positions related to data engineering are more focused on the development of software applications. They are also dependent on prior knowledge of specific technologies that are part of the organization’s mode of operation. But basically, a data engineer needs Skills in advanced programming especially python and Advanced skills in SQL and foundational knowledge with Postgres.
Potential Earning: $154,085
The job description of a data scientist and Machine learning engineer is somewhat intertwined. In most advanced organizations, a machine learning engineer is a data scientist who specializes in machine learning. In some other companies, a machine learning engineer is a software engineer saddled with the responsibility of using the records of the data scientists to develop handy software for maximum productivity in the organization. Although the job description for a machine learning engineer varies from company to company, all machine learning engineering roles will require basic knowledge in machine learning techniques and skills in data science programming.
Potential earnings: $152,049
Quantitative analysts are mostly called “quants”. They are responsible for using advanced statistical tools and analysis to solve problems, answer questions, and come up with future predictions related to risk management and finances. To be an outstanding Quantitative Analyst, you need Data Science programming skills and intermediate knowledge in the field of statistics. A solid understanding of models of machine learning and their application in solving financial problems and predicting Market positions you to be highly sought after as a Quantitative Analyst.
Potential Earning: $146,151
Data Warehouse Architect is a specialized subfield within data engineering for people who have passion for handling organizational data storage systems. To take up Data Warehouse Architect job roles, you need SQL skills and a firm command of other technical skills. The skills required vary based on the specification and demands of the employers. You must note, however, that you will not just be considered for Data Warehouse Architect positions for your Data Science skills alone but for your data management skills and SQL skills developed over time.
Potential Earning: $90,150
A Business Intelligence Analyst is a data analyst who is trained in analyzing business trends and market. The position requires skills in software-based data analysis, such as Microsoft Power BI. The main role of a Business Intelligence Analyst is analyzing data with the sole purpose of helping identify areas where the organization can improve. They also develop strategies for data gathering and business intelligence.
They are also responsible for keeping the business intelligence tools and the company’s database updated. As a BI Analysts, you can work full time, on a contract basis, or part-time.
Potential Earning: $97,021
Data scientists were fondly called Statisticians. The skills required vary from job to job, but all Statistician roles require a solid foundation in Statistics and Probability. The work experience as a Statistician gives you the rare privilege of applying your statistics knowledge to practice. There is a statistics-based work placement available in all sectors. You can improve your skills and become highly sought after in sectors like banking, market research firms, operational research institutes.
Potential Earning: $88,172
Business Analyst is a general term used to describe a wide variety of roles. But in its purest form, a Business analyst helps companies solve problems and answer questions for maximum productivity. The work of a Business analyst requires capturing, analyzing, and making sound recommendations based on the company’s data. Their job description does not only entail business analysis but includes system analysts, business system analysts, requirements engineers, product managers, enterprise analysts Etc, the opportunities available for a business analyst, are vast. You could also decide to go into business consultancy.
Potential Earning: $76,470
Marketing analysts are responsible for looking into marketing and sales data to assess and improve the effectiveness and efficiency of marketing campaigns. They have access to a wide range of data.
They also leverage software like Google Analytics, which allows analysis to be conducted easily without programming skills. The opportunities available for Marketing Analysts are vast. If you are skilled, you can rise to the highest echelon of your career to the Chief Marketing Officer Position, with a higher salary.
This post was last modified on August 1, 2022 6:59 pm