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Career options after an MBA in Data Science

Admin | September 14, 2022

Key takeaways:

  • Data science is one of the newest and fastest-growing fields in the job market. If you want to move up in your current job or find a new one, this is an interesting field to study.
  • Data architects use large programming tools to set up secure information systems that can manage large sets of electronic data for businesses.
  • Taking charge and showing initiative as you lead your team and deciding which technologies to use is fundamental to giving customers a good experience and ensuring everyone in the business is working well.

Discovering trends in raw data is the primary purpose of Data Science, which employs a wide variety of methods, including algorithms and machine learning techniques. Data professionals often provide explanations by looking back at past data. In contrast, a data scientist will not only do exploratory research to glean insights from it but will also use a wide range of cutting-edge machine-learning algorithms to predict when a certain event will take place. A data scientist will examine the data from several perspectives, some of which may be novel.

Individuals with an MBA in Data Science may pursue employment in diverse fields such as information technology, advertising, fashion, medicine, and the auto sector. An MBA holder may implement state-of-the-art analytical processes and techniques in a working firm.

Scope of data science as a vertical

Most MBA candidates consider data science and analytics careers among the most promising. The complex tasks need a wide range of merit-based commercial and technological expertise. To become proficient with a tool and ensure a good data science fresher salary, you just need to learn how to navigate its graphical user interface (GUI), which is far less challenging than learning how to write the actual code.

Graduates of master’s in business administration programs often specialise in data analytics and data science and are employable in many fields. MBA grads may succeed in marketing if they can effectively utilise data, comprehend advertising, and interpret consumer trends. You may use your risk analysis and uncertainty tolerance in the workplace by working in the financial services industry or with a management consulting business.  

So what careers can you pursue in this field, and what kind of income to expect from the MBA in data science salary?

Job opportunities after MBA in Data Science

Data Science is a booming industry and one of the most in-demand careers today. This is primarily because modern businesses need data scientists, data analysts, etc., owing to the increasing use and creation of data. In such a scenario, with an MBA in Data Science, your chances of getting hired by top companies will increase even more. 

Here are the top 10 careers you can opt for following an MBA in data science. 

  • Data analytics 

Full lifecycle analysis, including requirements, activities, and design, is a key part of a data analyst job. Data analysts will create new tools to analyse and report on the data. They will also keep an eye on quality assurance and performance benchmarks to see the potential for improvement.

  • Data science managers 

A Data Manager is one of many MBA data science jobs responsible for assessing the information requirements of a business or research institute and then meeting those needs via the use of technical expertise. After that, they take all the information they’ve gathered and arrange it. 

The primary function of a Data Manager is to evaluate data using their knowledge of analytics and the many coding tools at their disposal and to develop conclusions based on those analyses.

  • Product analyst 

When developing a product, a product analyst will work closely with the product manager to determine the target market’ specific needs and develop features accordingly. The product analyst’s job is to study the market and gather information to determine how to make the product even better and more useful to customers.

An effective product strategy is developed with a product analyst considering the company’s resources and goals.

  • Data science consultant 

A consultant data scientist offers many different jobs and salaries for masters in data science. This position is used for something entirely different from an in-house data scientist working on a specific dataset. By aiding their clients in developing a foundational comprehension of their data, data scientist consultants play an essential part in developing companies driven by data.

  • Data architect

An organisation’s data strategy is developed by a data architect, a specialist in data quality standards, data movement throughout the company, and data security. This data management expert’s strategic thinking ultimately turns corporate needs into technical specifications.

Demand for skilled data architects has increased as they play an increasingly important role at the intersection of business and technology.

  • Application architect

Application architects are crucial to any software development effort’s planning, execution, and evaluation phases. Architects are generally tasked with a larger range of duties, including managing user and business unit interactions and coordinating several applications. 

They need strong interpersonal and communication skills to effectively collaborate with everyone, from software project managers to end users. In most cases, this also entails maintaining documentation, creating guidelines for application development, and educating workers.

  • Market research analyst 

Researchers in this field analyse customer tastes to inform businesses how to improve their offerings via design, promotion, and distribution. A large contingent of contract market researchers works for consulting businesses. Some people are employed by companies directly as part of the product or consumer marketing team.

Because of the widespread use of market research, these analysts can work in virtually any sector of the economy. Most analysts put in a full-time shift during normal business hours. Some people have to operate under intense time constraints and strict deadlines.

  • Statistician 

Statisticians are experts in using statistical tools and models to address practical issues. They help with numerous business decision-making processes by collecting, analysing, and interpreting data. Statisticians are highly sought after in various fields, including business, healthcare, government, the physical sciences, and the environment.

Naturally, the industry and the company in which a statistician is employed will directly impact the day-to-day responsibilities that person is tasked with doing.

  • Machine learning engineer 

Engineers specialising in machine learning play a crucial role in today’s data science teams. Their work includes developing new AI systems, maintaining and bettering the AI already in place, and studying and developing the AI responsible for machine learning.

A machine learning engineer works closely with the data scientists who create the models used in AI system development and the engineers and scientists who put those models into action.

  • Chief data officer 

The CDO leads the Data and Analytics Department’s vision, strategy, and execution. The Chief Data Officer ensures the department is operating properly to facilitate long-term growth and profitability for the business and increase internal efficiencies by enhancing data structures, maintaining high levels of data cleanliness and insight, and ensuring effective data governance and process.

They are always searching for new methods to leverage data analytics to develop possibilities, such as predicting consumer behaviour or identifying new revenue prospects. Revenue growth is a priority.

DesignationAverage Salary (INR)
Data Analytics8 LPA
Data Science Managers15 LPA
Product Analyst  17.1 LPA
Data Science Consultant15.9 LPA
Data Architect22 LPA
Application Architect24 LPA
Market Research Analyst  6 LPA
Statistician8 LPA
Machine Learning Engineer  10 LPA
Chief Data Officer  26 LPA
Source: Glassdoor

Top data science recruiters

Here are some of the top data science recruiters who may help you kick-start your career in The field. 

  • Deloitte

Deloitte has been a consultant, financial advisor, tax advisor, auditor, and enterprise risk manager since 1845. For its cross-departmental analytics, Deloitte turns to data science. To help their clients, Deloitte’s data scientists sift through massive amounts of information.

  • Flipkart

As Flipkart expands and creates more data, it needs the services of data scientists. The data scientists at Flipkart are concentrating on improving NPS by collecting and analysing useful business knowledge. Information unique to a certain domain is used to direct data creation and upkeep.

  • Amazon

A lot of data scientists work at Amazon. Experienced in areas like supply chain optimisation, fraud and false review identification, multivariate testing, inventory and sales forecasting, and more, the data scientists at Amazon India are essential to the company’s success. The analytic capabilities of data science are applicable outside the realm of marketing.

  • Citrix 

This US firm operates in the Indian networking, cloud computing, and software markets. Citrix relies on data science for strategic planning and foresight.

  • Accenture

A well-known multinational company like Accenture needs competent data scientists. And if you are an individual who likes coming up with ideas and putting them into action by using cohesive data sets, then Accenture can be a great place to work for you. 

READ MORE: MBA Analytics & Data Science course syllabus

Career growth opportunities in data science

If you are looking for a career in data science or to grow by trying out new opportunities, then keep reading to know more. 

  • Junior data scientists 

Becoming a data analyst or junior data scientist demands expertise. Solid maths skills, data visualisation and cleaning skills, and computer language experience may be helpful.

Junior data scientists collect, filter, integrate, and load data. They focus on predictive analysis and often apply existing statistical models or follow a senior data scientist’s direction.

  • Intermediate-level data scientists 

Entry-level data scientists often work in that capacity for the first two years of their careers with an average salary for a master’s in data science. Data scientists at the intermediate level are trusted with more responsibility and fewer oversights. It is expected that they can conduct exploratory data analysis and construct the necessary statistical models for problem-solving on their own.

On the other hand, senior data scientists may provide opportunities for mid-level data scientists to collaborate on cutting-edge applications of machine learning and artificial intelligence.

  • Senior data scientists

Senior data scientists have worked in the area for three to seven years. Senior data scientists combine middle-level data scientists’ statistical models with cutting-edge methodologies to solve challenging problems. It guarantees the future scope of a master’s in data science field.

Senior data scientists help enhance an organisation’s procedures and deliver data-driven insights to customers and higher management. Senior data scientists typically assist and train new data scientists.

  • Managerial data scientists 

Managers of data science teams must have a firm grasp of the entire picture, from staffing to objectives to KPIs.

As it is for managers in any field, the goal is to foster an atmosphere conducive to productivity without stifling innovation. A data science manager’s ability to keep up with the latest innovations and implement them within their team is crucial to maintaining a competitive advantage.

Managers in the field of data science often have between one and three years of mentoring and coaching and seven years of experience as data scientists.

Bottom line

For those interested in a stable Data Science career, consider looking into the online MBA program at Online Manipal, designed by Manipal Academy of Higher Education (MAHE). Businesses are making more and more data that can be used, creating more jobs and more opportunities for a good salary. Forecasting the market, improving sales conversion rates, and keeping track of a company’s growth are all things that require the help of experts.

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