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Data Science Jobs - The Busting Ways To Secure Attractive Packages In Health Care?

By Krishna Kumar Category Data Science Reading time 10-12 mins Published on Apr 6, 2022

Know the Importance of Data Science in the Medical Industry

Got your master's degree in genetic counseling, occupational therapy, health administration, or any other healthcare domain?

Are you dreaming to join a renowned organization with a six-figure salary package?

But wait. It takes a lot of work these days. More than 70% of lucrative healthcare degree holders still need to secure their position.

What's the reason? They all are well qualified and experienced enough.

Some people work almost 24X7 with a package of 3 to 4 LPA, even after getting a master's degree. However, there is a contrasting side to it as well. A few enjoy 10 to 12 LPA, with an average of 3 to 4 years of experience in the same domain.

This industry has had a new 'must-have' skill requirement for the last few years. It's Data Science and AI. At present, data science jobs are the hottest deal within the healthcare industry.

Data science and AI have changed the healthcare sector's job market direction.

This blog will help you find the root path to secure your jobs in the healthcare sector.

First, let's answer the questions that are coming to the mind of most readers right now.

Are data science jobs in demand across the healthcare industry?

Nowadays, when we think about the healthcare system, we visualize highly upgraded tech and AI-powered patient care services. Take as an example the SMART clinical apparatus used for advanced medical research. However, for the last few years, the healthcare industry has been in the topmost position on cyber criminals' hit list.

During the COVID-19 pandemic in India, lots of COVID patients' personal data got leaked from several governments and NABL healthcare portals. These included information such as residential addresses, contact numbers, Aadhar numbers, etc. Such incidents caused the suspension of many frontlines and associated employees.

Wrong handling of data and the need for more data management expertise was the key reason behind it.

  • Employees from a non-data science background in healthcare are at risk of job loss. This is because many drug companies compete for better regional sales worldwide. And in such cases, sharp data analytical skills become mandatory.

  • Candidates with core data science skills and analytical knowledge are in quite a safer position. The Pharma industry needs them as they need more such talent.

Data science healthcare jobs are one of the beneficial careers to target in modern-day scenarios. When you look at it, healthcare is one domain where data science has mainly been used for a very long time. The clinical industry can run efficiently by applying some data science concepts. So there is no doubt that you will have a great scope in the healthcare industry as a data scientist.

What is the role of data analytics in the healthcare sector?

According to a study from daily mail UK, the amount of data produced per day by all human bodies is two terabytes. This data includes brain activity, stress levels, heart rate, sugar content, blood pressure, and more.

  • There are now more advanced technologies for processing such large amounts of data, one of which is AIoT. It helps monitor the patient's health based on the recorded data, remotely.

  • With the help of data science in medicine, it has become possible to detect the symptoms of a disease very early. Doctors can remotely monitor the patient's condition with various innovative tools and technologies.

Nowadays, any industry generating large amounts of data needs data science.

The medical industry produces large datasets of helpful information about patient demographics, treatment plans, health check results, insurance, and more. The data collected by Internet of Things (IoT) devices has attracted the attention of data scientists.

All these techniques and methods make it essential to have a career as a healthcare data scientist.

But do data science jobs in healthcare provide just as excellent a salary as working in a product-based MNC?

To find the answer, let's look at what your data science salary will depend upon.

What drives a Data Scientist's Salary?

We all know and hear that specialists in this field make a lot of money. But we must be aware that it varies from domain to domain. The data scientist's salary may depend on these factors.

  • The need for data science in the industry.

  • The type of role that you will be playing.

  • The field-relevant skills.

  • Your skill set as a data scientist.

  • Years of experience you have as a specialist.

How much is a Data Scientist healthcare package?

This is the number one question everyone has regarding domain-specific data science jobs. However, it would be best if you do not worry, as organizations offer big bucks to specialists.

On average, a data science specialist working in the medical field earns about Rs. 50 lakhs annually in India.

Based on Companies

Some of the popular healthcare companies and how much they offer in their data science jobs.

  • GE Healthcare offers Rs. 18.6 lakhs per annum.

  • SCIO Health Analytics provides Rs. 7.10 to 8.64 lakhs per annum.

  • Novartis Healthcare offers Rs. 21.7 lakhs per annum.

  • Dr. Reddy provides Rs. 12.8 lakhs per annum.

A chart lists the salary scales of  data scientist jobs in different cities such as, 
New Delhi: INR 20,000/month for SAS programmers
Chennai: INR 70,000/month for pharmaceutical Data Analysts
Bangalore: INR 12,89,859/P.A Data Analyst
New Delhi: INR 18,70,000/P.A for Data Analysts

Based on Location

Packages on the basis of the location where you will apply for data science jobs.

  • Analysts in the New Delhi region make Rs.18,70,000 a year.
  • Data analysts in Bangalore make Rs. 12,89,859 a year.
  • Medicinal data analysts in the Chennai region earn Rs. 70,000 a month.
  • SAS programmers in the New Delhi region are reported to have made Rs. 20,000 a month.
Country       Alcoholic Animal Protein Animal Fats Aquatic Protein Cereals Eggs
Afghanistan 0 21.6397 6.2224 0 8.0353 0.6859
Albania 0 32.0002 3.4172 0 2.6734 1.6440
Algeria 0 14.4175 0.8972 0 4.2035 1.2171
Angola 0 15.3041 1.313 0 6.5545 0.1539
Antigua and Barbuda 0 27.7033 4.6686 0 3.2153 0.3872
Argentina 0 30.3572 3.3876 0 1.3316 1.5706
Armenia 0 29.6642 6.2619 0 2.5060 1.6196
Australia 0 24.1099 4.683 0 0.9988 0.7817
Austria 0 27.0268 12.0517 0 1.2297 1.2147
Azerbaijan 0 32.1198 7.7987 0 5.4481 2.0197
Bahamas 0 30.2259 4.56 0 3.6327 1.2829
Bangladesh 0 9.8365 1.2369 0 6.9946 1.2664
Barbados 0 25.8451 3.9733 0 2.1187 1.213
Belarus 0 23.8181 7.8662 0 4.8075 1.5772

Applications of Data Science in Healthcare

The uses of data science in healthcare are immense. Let us look at some of the most critical applications.

1. Medical Image Analysis

Doctors find the defects in the human body using medical imaging tests like x-rays, ultrasonography, MRI (Magnetic Resonance Imaging), and CT scans. Doctors can now find micro-sized deformities in scanned images with deep learning technologies.

Other image refining methods include support vector machines, edge detection, big data platforms (Hadoop), and more that help doctors develop effective treatment strategies.

Algorithms used in medical image analysis

  • Anomaly Detection Algorithm: This helps identify conditions such as fractures and dislocations.

  • Image Processing Algorithms: Image processing algorithms are useful for image analysis, improvement, and noise reduction.

  • Descriptive Image Recognition Algorithm: This visualizes and extracts data from images. The data is then used to interpret and form larger images (for example, merging images from brain scans and naming them accordingly).

2. Predictive Analytics

A predictive analytics model based on data science can predict a patient's health. It can help develop strategies for the appropriate treatment for the patient, playing a vital role in the medical industry.

A predictive model can learn from previous data and provide meaningful outcomes by finding patterns in the data. Through this data-driven analysis, doctors can find solutions to prevent common diseases in the general public.

Algorithms used in predictive analysis

  • Classification Model: It categorizes patient data according to requirements and needs.

  • Clustering Model: The clustering model sorts the data into separate nested intelligent groups based on similar attributes. Clustering is a valuable technique in the medical sector for detecting patterns and organization in labeled and unlabeled information.

  • Forecast Model: As one of the most widely used predictive analytics models, forecasting models involve predicting metric values ​​and estimating new data numbers. These numbers are based on insights from historical data.

3. Genomics

In the past, genomics research was a tedious and time-consuming task before the advent of robust data analysis techniques. Now, data science applications in healthcare and genomics have made that task convenient.

With the help of various data science and big data tools, healthcare data scientists can analyze human genes to obtain insights with less effort and time. Big data tools such as MapReduce, SQL, Bioconductor, etc., can speed up genetic sequencing analysis.

In addition, researchers can easily find the drug that best responds to a particular genetic problem or a particular type of gene.

Algorithms used in Genomics

  • V Framework: The V framework categorizes volume, velocity, and variety of data. Genomics is used to analyze the current data concerning other applications in data sciences.

  • 4M Framework: This concept describes the overall process of systems biology, closely related to genomics in quantity measurement large-scale mining.

What skills does it take to become a Healthcare Data Scientist?

Healthcare data scientists manage and process vast and confusing health data records from various sources, putting them together in an evaluable format. Innovative skills are needed to unleash knowledge from complex health data and address some of the world's biggest health problems today.

  • They require knowledge about analyzing data using statistical machine-learning algorithms and extracting valuable insights from it.

  • They also require a solid computational or math background.

  • A solid quantitative background provides emerging health data scientists entry into healthcare and medical research areas. These roles are ideal for students with bachelor's degrees in mathematics, statistics, or related disciplines.

  • The primary programming languages ​​required in most health-related data science fields include Python, R, and, in some cases, SAS.

  • Upcoming healthcare data scientists need a deep understanding of statistics, linear algebra, quantitative methods, and calculus.

Of the other skills needed is communicating results with different healthcare professionals. Health data scientists communicate with other data scientists about

  • How to use the data and insights?

  • Discuss with clinicians to understand the illness they are studying.

  • Communicate with laboratory scientists and, most importantly, patients.

It would help if you shared clearly and transparently with the general public.

What the future holds for healthcare data scientists?

Due to technological advancements and the digitization of most sectors, there is also a strong need for improvements in the healthcare industry. For reducing treatment costs, providing better health results, and handling the rapid population expansion, data science will prove to be a greater tool.

Pharma and the medical industry will always be relevant as long as humans exist. Various data science jobs in the medical industry also opened up due to the advancements. To fully utilize this opportunity, you will need an in-depth knowledge of data science and its usage in healthcare. Through real-time applications, you can easily secure an attractive package in the medical field.

Learnbay's Data Science and AI Program will aid you in understanding valuable projects and use cases in the healthcare/medical domain.