Data Scientist Salary: Data science is the latest buzzword in the market. With market forces relying on digital capabilities, mobile phones and digital gadgets a reliable sources of data.
The data set for business development, product modifications and research has become complex.
The role of data science is the most important for development and growth in all aspects. Candidates with an aptitude for coding and a good command of maths can get high-paying jobs with high packages of around 22 lakhs in the data science field.
The space below has the details on Data science salary, skill set and job profile for you to make easy and wise choices related to your career.
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Data scientists acquire and analyse enormous sets of organised and unstructured data. A data scientist’s job entails a mix of computer science, statistics, and mathematics. They interpret the outcomes of data analysis, processing, and modelling to generate actionable plans for businesses and other organisations.
Data scientists are analytic professionals who use their knowledge of technology and social science to identify patterns and handle data. They identify solutions to corporate difficulties by combining industry knowledge, contextual insight, and scepticism of established assumptions.
As a result, data scientists are a mix of computer scientists, mathematicians, and trend analysts. Data scientist salaries in India are among the highest due to great demand.
A data scientist’s job entails deciphering complex, unstructured data from sources like smart devices, social media feeds, and emails that don’t fit neatly into a database.
The average salary for a data scientist is Rs.698,412 per year.
With less than a year of experience, an entry-level data scientist can make approximately 500,000 per year.
Data scientists with 1 to 4 years of experience may expect to earn about 610,811 per year.
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- Algorithms, statistics, mathematics, and machine learning knowledge are all important.
- R, Python, SQL, SAS, and Hive are examples of programming languages that are required to be known by a Data Scientist.
- Communication skills are required to properly communicate the results to the rest of the team.
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Data Scientist's Job Roles
Data scientists work closely with business stakeholders to learn about their objectives and how data may help them achieve goals. They create algorithms and predictive models to extract the data that the business needs, as well as help evaluate the data and share findings with peers. Along with R, Python has demonstrated its ability to sort data according to both generic and specialized needs. Python data science programmers in India make more than both software developers and DevOps programmers. The reason for this is that data gathering, data cleansing, and data processing are becoming increasingly popular in today’s world, as businesses require data to obtain market and customer data.
Data Scientists Responsibilities
- Taking massive amounts of structured and unstructured data and turning it into useful information.
- Identifying the data analytics solutions that have the most potential to propel businesses forward.
- Using data analysis tools such as text analytics, machine learning, and deep learning to uncover hidden patterns and trends.
- Data cleansing and validation to improve data accuracy and efficacy.
- Data visualization is used to communicate all of the positive observations and discoveries to the company’s stakeholders.
1. Growing Demand
One of the most in-demand jobs for 2021 is data science. Data science and analytics are expected to employ more than 11 million people by 2026. India is the second-largest source of data scientist jobs after the United States. One of the main reasons for the high salaries of data scientists in India is the high demand.
2. High-paying jobs with a wide range of responsibilities
Not only is there a high demand for data scientists, but the types of jobs available are also plentiful. The demand for data scientists is rapidly increasing, and there is a substantial supply shortage. Due to a shortage of essential skill sets, there are a large number of vacant job openings all around the world. Because of the severe scarcity of talent, this is an excellent time to enter this sector.
Changing working environments
The future workplace is being shaped by data science. More and more routine and manual chores are being mechanized thanks to artificial intelligence and robotics. As people take on more critical thinking and problem-solving roles, data science technologies have made it easier to educate robots to perform repetitive jobs.
4. Increasing product quality
Machine learning and Artificial Intelligence have allowed businesses to personalize their offers and improve client experiences.
They are thriving in every industry, from information technology to health care, and from e-commerce to marketing and retail.
Because data is a company’s most valuable asset, Data Scientists play a critical role as trusted advisers and strategic partners to management.
They look for relevant information in the data that might help them improve their speciality, determine their desired target audience, and plan future marketing and growth initiatives.
5. Contributing to the greater good
The healthcare industry has been transformed by predictive analytics and machine learning. Early diagnosis of cancers, organ defects, and other diseases is possible because of data science.
6. Evolving Field
Because of the growing demand for data all around the world, data science is rapidly evolving.
Data scientists have a wide range of skills that may help firms make better strategic decisions by leveraging data and information.
They have great possibilities to engage with data and experiment to find the best solutions for organisations.
Big Data, Artificial Intelligence (AI), Machine Learning (ML), as well as some newer technologies like Blockchain, Edge Computing, Serverless Computing, Digital Twins, and others that employ various practises and techniques within the Data Science industry, are just a few of the new exciting fields that are emerging within this field.
7. Interesting Job role
Human behaviour is the primary focus of data scientists. As a data scientist, you’ll largely be working on how humans operate, from designing a chatbot to evaluating user experience online. As a result, you’ll be directly participating in one of the century’s most important endeavours.
8. Extensive job experience
You can experiment with a wide range of fields as a data scientist.
You’ll be able to work on a variety of geeky projects, ranging from e-commerce enterprises to startups to production companies to renewable energies to traffic optimization.
As a result, you’ll have a lot of “horizontal mobility” in the field.
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Based on Experience
Let’s look at how the salary of a Data Scientist in India differs based on experience.
Because of the strong association between years of work experience and higher-paying salaries, a career in data is particularly appealing to young IT workers. We’ll look at how data scientist salaries rise with experience in this section. In India, the average entry-level data scientist income is 511,468 rupees per annum for a recent graduate.
A data scientist in their early career with 1-4 years of experience earns an average of Rs.773,442 per year.
Employees with 5 to 9 years of experience can expect to earn between INR 12 and 14 lakhs per annum. The average mid-level data scientist income, according to payscale, is Rs1,367,306 per annum. A highly experienced employee with decades of expertise or who has held management positions might expect to earn anywhere from INR 24 lakhs per annum to a healthy crore!
With a transition/promotion from the role assigned to them to a higher one, a data analyst’s income improves by 50%.
Based on Location
Mumbai has the most job prospects and the highest yearly data scientist salaries in India for data innovators, followed by Bangalore and New Delhi. However, because Bangalore is India’s startup capital, it boasts the most startup job opportunities. Because Bangalore is considered the centre of India’s tech industry, a data scientist’s compensation is likely to be higher than in other locations.
A data scientist’s income in India varies depending on where they work:
|Mumbai||Rs.788,789 per annum|
|Chennai||Rs.794,403 per annum|
|Bangalore||Rs.984,488 per annum|
|Hyderabad||Rs.795,023 per annum|
|Pune||Rs.725,146 per annum|
|Kolkata||Rs. 402,978 per annum|
Based on Employer
Without a doubt, prominent organisations are at the top of the list of the highest-paying data positions. They also have a reputation for raising salaries by 15% per year. Top firms pay data scientists in the following ways:
|IBM Corp||INR 1,468,040 per annum|
|Accenture||INR 1,986,586 per annum|
|JP Morgan Chase and Co||INR 997,500 per annum|
|American Express||INR 1,350,000 per annum|
|McKinsey and Company||INR 1,080,000 per annum Wipro|
|o Technology||INR 1,750,000 per annum|
Based On Skills
To get a job paying this well, you’ll need to have more than a Master’s degree and be conversant with the languages and tools used to manage data. Here are some additional AIM tidbits:
- Knowing R is the most crucial and sought-after expertise, followed by Python. Python salary in India is expected to be around 10.2 lakhs INR per annum
- When a Data Analyst knows both Big Data and Data Science, their income rises by 26%, compared to when they only know of one.
- SAS users are paid in the range of INR 9.1-10.8 lakhs per annum, whereas SPSS professionals are compensated in the range of INR 7.3 lakhs per annum.
- Machine Learning salaries in India start at roughly 3.5 lakhs INR and can rise to 16 lakhs INR as you advance in the industry. Python is one of the most popular languages for machine learning, and Python developers in India earn some of the best salaries in the world.
- Artificial Intelligence knowledge can assist in advancing your career in general. If you are a beginner in this field, the Artificial Intelligence pay in India is not less than 5-6 lakhs INR.
So now is the time to improve your data abilities to maximize your earnings!
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Mu Sigma, Accenture, and Tata Consultancy Services Limited are the top respondents for the job title Data Scientist in India.
has the highest reported salaries.
Accenture and HCL Technologies Ltd.
are two more firms that pay well for this position.
Mu Sigma pays the least.
Tata Consultancy Services Limited and IBM India Private Limited are likewise on the low end of the range.
Salaries offered by the top 5 countries are as follows-
The United States of America is at the top of the list of countries that give high salaries to data scientists who are willing to work for them. The average yearly salary for data scientists in the United States is $120,000 per year. The pay is higher than in any other country for data scientists.
Australia is ranked second on the list of countries that pay data scientists well. This is evidenced by the influx of data scientists from Australia and other countries to the United States. The average salary for a Data Scientist is between AU$75,233 per year- AU$121,578 per year based on one’s experience.
Nobody could have predicted that Israel would become a major IT centre, with a plethora of career opportunities for both novice and seasoned data scientists. In Tel Aviv, Israel, working data professionals earn roughly $88,000 per year
You’re in for a treat if you’re seeking data science employment in Canada. Data scientists in Canada make roughly $81,000 per year. The starting wage for a data scientist is $77,870 per year and can rise to $117,750 per year.
In Germany, people looking for data science employment might earn up to 5,960 euros per month.
Working data scientists in Germany earn between 2,740 and 9,470 euros per month.
1). Preparation of Data
Before using data for analysis, data scientists spend roughly 80% of their time cleaning and preparing information to improve its quality – that is, to make it accurate and consistent. However, 57 per cent of them regard it to be the most difficult aspect of their professions, describing it as time-consuming and monotonous. Daily, they must process terabytes of data across numerous formats, sources, functions, and platforms while keeping track of their activities to avoid repetition.
Adopting developing AI-enabled data science solutions like Augmented Analytics and Auto feature engineering is one way to address this problem. Data scientists can be more productive by using Augmented Analytics to automate tedious data cleansing and preparation chores.
2) A variety of data sources
More data sources will be needed by data scientists to make meaningful judgments as firms continue to use various sorts of apps and technologies and generate various formats of data. This approach necessitates manual data entry and time-consuming data searching, which results in errors, repetitions, and, ultimately, incorrect conclusions.
To rapidly access information from many sources, organisations require a single platform that is integrated with different data sources. Data in this unified platform can be pooled and regulated effectively and in real-time, allowing data scientists to save a significant amount of time and effort.
3) Data Protection
Cyberattacks are becoming more widespread as firms migrate to cloud data management. This has resulted in two key issues:
- Confidential information is at risk.
- As a result of recurrent cyberattacks, regulatory norms have grown, lengthening the data consent and utilisation processes, further aggravating the data scientists’ displeasure.
To protect their data, businesses should use powerful machine learning-enabled security platforms and implement additional security measures. Simultaneously, they must maintain rigorous adherence to data protection regulations to prevent time-consuming audits and costly fines.
4) Recognizing the Business Issue
Data scientists must first completely understand the business challenge before undertaking data analysis and developing solutions. Most data scientists take a mechanical approach to this, jumping right into examining data sets without first identifying the business problem and goal.
As a result, before beginning any analysis, data scientists must follow a specific methodology. The workflow should be created after consulting with business stakeholders and include well-defined checklists to aid in problem identification and understanding.
5) Effective Non-Technical Stakeholder Communication
Data scientists must be able to communicate successfully with corporate leaders who may not be aware of the complexity and technical language involved in their work. If the CEO, stakeholder, or client is unable to comprehend their models, their solutions are unlikely to be implemented.
This is a skill that data scientists can develop. They can use concepts like “data storytelling” to provide their communication with a more systematic approach and a compelling narrative to their analyses and visuals.
6) Metrics and KPIs That Aren’t Defined
Management teams’ lack of awareness of data science leads to unrealistic expectations of data scientists, which has an impact on their performance. Data scientists are supposed to come up with a magic bullet that will solve all of the company’s problems. This is quite ineffective.
As a result, every company should have:
Data scientists must use well-defined metrics to assess the accuracy of their analyses.
Appropriate business KPIs to assess the impact of the analysis on the business.
Data scientists are the most sought-after experts in the market, notwithstanding the challenges. Being a good data scientist needs not only technical skills but also a full understanding of business requirements, collaboration with diverse stakeholders, and persuading business executives to act on the information provided.
There are no degrees that will qualify you as a trustworthy data scientist.
There are no prerequisites for becoming a credible data scientist, but neither are there any prerequisites for becoming a credible data scientist.
Unlike several other occupational titles, “data scientist” is not a protected title.
Medical doctors, nurses, and lawyers, for example, have stringent requirements.
Data science, however, does not.
2. How much money can you make in data science?
The average data scientist salary in India is Rs. 698,412. With less than a year of experience, an entry-level data scientist can make approximately Rs. 500,000 per year. Data scientists with 1 to 4 years of experience may expect to earn about Rs. 610,811 per year. In India, a mid-level data scientist with 5 to 9 years of experience earns Rs.1,004,082. Senior-level data scientists in India earn roughly 1,700,000 per year as their expertise and talents increase.
One of the highest-paying careers in data science. Data Scientists earn an average of Rs.116,100 a year, according to Glassdoor. As a result, Data Science is a very lucrative career choice.
In 2020, there are expected to be 2.7 million open positions in data analysis, data science, and related fields (source: IBM). Employer demand for both data scientists and data engineers is expected to climb by 39% by 2020.
Data Scientists is A “Lucrative Career”.
In recent years, the input of data has increased at an exponential rate. As massive amounts of data began to flow into data centres, numerous new opportunities arose, particularly in data science. Digital transformation was the only option due to technological advancements in the data landscape. As more businesses embrace digitisation, they are looking for employees to fill data science and related positions. Data science professionals are in high demand all across the world. These job prospects are likely to increase significantly beyond 2021, with more than 1.5 lakh additional jobs being created. Glassdoor has ranked data science as the number one job in the United States for the past four years, hence it is a good career option.
Data science is a booming area, and many people may be considering a career change due to lucrative work opportunities. You must, however, be able to explain your professional change. You may become a data scientist without any prior experience if you keep these things in mind.
To become a data scientist, follow these three steps: Obtain a bachelor’s degree in information technology, computer science, mathematics, physics, or a related discipline; Obtain a master’s degree in data science or a closely related discipline; Obtain experience in the field in which you wish to work (ex: healthcare, physics, business).