7 Jobs after completing an online Masters in Data Science

7 Jobs after completing an online Masters in Data Science

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Data Scientists or Data Analysts are professionals with an aptitude for mathematics and statistical analysis who collect and interpret large amounts of data in order to identify business trends, opportunities and threats. Often holding a degree in computer technology, Data scientists are employed in organisations that use data to drive business.

As the Data Analytics profession experiences a rapid demand growth, companies of every scale are actively pursuing candidates with degrees in Data Science. Some of the best UK universities have identified the skill gap that is currently evident in the business analytics industry and are offering online Masters in Data Science courses to help professionals upgrade their data skills.

Is data science right for you?

Data science is a potentially fulfilling career path, but it’s important to evaluate if it’s the appropriate fit for you based on your talents, interests, and long-term objectives. A solid background in mathematics and statistics, competence in programming languages like Python, R, and SQL, and familiarity with data processing and analytic tools are among the essential technical abilities. Effective data interpretation and communication depends on cognitive abilities like curiosity, problem-solving, critical thinking, and analytical skills.

With strong demand and attractive compensation, data science provides a wide range of employment options across multiple industries. Data science might be the best job choice for you if you have these abilities and qualities and are enthusiastic about a dynamic, well-paying profession in a quickly developing field. In order to ensure this field fits with your long-term ambitions, it’s important to carefully assess your personal abilities, interests, and career objectives.

Jobs after completing an online Masters in Data Science:

Data Architects

The role of a Data Architect encompasses the design and implementation of an organisation’s overall data architecture, ensuring that data is stored, managed, and accessed in a manner that supports the business goals of the organisation.

The average annual salary for a Data Architect in the United States is approximately $130,000, with a range of $100,000 to $180,000 depending on experience and location. Job titles include Chief Data Architect, Enterprise Data Architect, Solution Architect, and Data Governance Architect.

Data Engineers

Building and maintaining the infrastructure necessary to enable data-driven applications and analytics falls within the purview of data engineers. They strive to make sure that information is gathered, handled, and kept in a way that makes analysis both successful and efficient.

A data engineer’s annual compensation in the US ranges from $90,000 to $160,000, depending on experience and region. The average salary is approximately $123,000. Job titles include ETL developer, data pipeline engineer, data infrastructure engineer, and big data engineer.

Business Intelligence Analyst or BI Analyst

The job of business intelligence analysts is to analyse data so that businesses may make well-informed decisions. They search for patterns, trends, and insights that can guide operational enhancements and strategic planning.

A business intelligence analyst’s annual compensation in the US ranges from $60,000 to $120,000, depending on expertise and region. The average salary is approximately $85,000. Job titles include Consultant in Analytics, Business Analyst, Data Analyst, and Business Intelligence Specialist.

Data Scientists / Data Analysts

Utilising cutting-edge statistical and machine learning methods, data scientists and analysts are in charge of drawing conclusions from data. They seek to recognise trends, anticipate outcomes, and assist in decision-making.

In the United States, a data scientist’s annual compensation typically ranges from $113,000 to $150,000, depending on their region and level of experience. Job titles include Machine learning engineer, predictive analytics specialist, data scientist, and data analyst.

Data Manager

The task of supervising the gathering, storing, and administration of an organisation’s data assets falls to data managers. They strive to guarantee that data is safe, easily accessed, and in line with the goals of the company.

In the US, a data manager’s annual compensation ranges from $80,000 to $140,000, depending on their region and level of expertise. The average wage is roughly $105,000. Job titles include Data Steward, Data Operations Manager, Data Governance Manager, and Data Quality Manager.


The job of statisticians is to gather, examine, and explain data so that businesses may make wise decisions. They try to spot trends, put theories to the test, and let stakeholders know what they discover.

In the US, a statistician’s annual compensation ranges from $70,000 to $120,000, based on expertise and geographic region. The average wage is approximately $92,000. Job titles include Statistical Consultant, Research Statistician, Quantitative Analyst, and Biostatistician.

Data Modeler

The task of creating and executing the data models that underpin an organisation’s analytics and data-driven applications falls to data modelers. They try to make sure that data is organised so that it can be analysed effectively and efficiently.

In the US, a data modeler’s annual compensation ranges from $80,000 to $130,000, depending on their region and level of experience. The average wage is roughly $100,000.
Job titles include Information architects, database architects, data modelling specialists, and data modelling consultants.

What kinds of projects can data scientists work on?

  • Working with previous data, data scientists can create models to predict future trends, behaviours, or results. This is known as predictive analytics. Data scientists can work on a wide range of projects in a variety of industries.
  • Recommendation systems: Developing algorithms that make recommendations for goods, information, or services in response to user preferences and actions.
  • Natural language processing: Deciphering and drawing conclusions from unstructured textual data, including social media postings or consumer evaluations.
  • Computer vision: Creating models for the analysis and interpretation of visual data, such as pictures or videos, in order to use it for tasks like image classification or object recognition.
  • Anomaly detection: Finding odd trends or anomalies in data that could point to fraud, malfunctions in the system, or other problems.
  • Optimisation: Applying data-driven methods to enhance decision-making, resource allocation, and business processes.
  • Personalisation: Creating models to customise experiences, goods, or content for specific consumers or clients.

Education for Data Science

To become a data scientist, you normally require a solid educational background in subjects like mathematics, statistics, computer science, or a similar technical degree. A master’s or Ph.D. in data science, statistics, or a related discipline is often held by data scientists.

The following are a few typical data science educational pathways:

  • Online or boot camp-style data science training programs.
  • Bachelor’s degree in computer science, mathematics, statistics, or a related field.
  • Master’s degree in data science, analytics, or a related subject.
  • D. in data science, statistics, or a related field.

Data scientists frequently need to acquire a wide range of abilities in addition to a formal degree, such as programming, data manipulation, statistical analysis, machine learning, and effective communication. In a field that is changing quickly, professional development and ongoing education are crucial.

Written by Loulwa Kazoun,

Corporate Learning Partnerships Manager, Stafford Global


Loulwa Kazoun LinkedIn


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