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Table of contents
1.
Introduction🌼
2.
About the Job📽️
3.
Skills, Qualifications, and Experience Required🤹
4.
Role and Responsibilities⛑️
5.
Salary and Perks💸
6.
Roadmap and Carrier map for the job🛣️
7.
Resources🔨
8.
Frequently Asked Questions❓
8.1.
At Microsoft, what does a data scientist do?
8.2.
What does Microsoft mean by "Applied Scientist"?
8.3.
Are data scientists employed at Microsoft?
8.4.
What are data and applied scientists?
8.5.
Do data scientists simply practice statistics?
9.
Conclusion✉️
Last Updated: Mar 27, 2024

Data and Applied Scientist at Microsoft

Author Shiva
0 upvote

Introduction🌼

Microsoft, founded by Bill Gate, is a well-known Tech Giant. Microsoft Corporation is a multinational technology business based in the United States that manufactures laptops, home appliances, and software for computers. Some of Microsoft's most well-known software products are the Windows line of operating systems, the Office suite, and the web browsers Internet Explorer and Edge. The company started off small, concentrating on local commerce, and over time, grew into the powerhouse it is today.

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Microsoft has numerous groups, each with its own data scientists, because it is a massive organization with over 100,000 employees (part of the Data & Applied Sciences discipline). Although the objectives and tasks are different, common occurrences bring people together.

About the Job📽️

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Simply described, an Data and Applied Scientist is someone who applies theoretical, conceptual frameworks and algorithms to the underlying data to offer actionable solutions to business challenges.

Data and Applied Scientist is an individual who processes, models, analyze, and evaluates data of any kind to provide insightful conclusions and aid in resolving business issues (and, in most situations, to uncover new ones!).

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Skills, Qualifications, and Experience Required🤹

The Skills and Experience required by a Data and Applied Scientist at Microsoft is following:

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  • Bachelor’s / Master’s or Doctorate in Related Field
    Bachelor’s, Master’s, or Doctorate in Computer Science with Machine Learning or a related field is a plus. Experience of 5 years or more in the area is preferred.
     
  • Solid Expertise in Data Science Algorithms
    Up to sophisticated machine learning models, from fundamental analysis. Having a thorough understanding of how each of these functions is advantageous but not necessary. Pareto's Principle applies to many different fields, including  Data science, and it states that 80% of business problems may be resolved using 20% of the strategies or methodologies.
     
  • Strong Understanding of the Tools/Software
    If you don't know how to apply Data Science methods to the underlying data, then understanding them isn't much use. This is accomplished using coding platforms with machine learning and statistical software as well as visualization tools that aid in interpretation and analysis.
     
  • Solid Grasp of Industry Knowledge
    For Applied Data Scientist, the industry is essential. There are times during analytics projects when it is necessary to steer the analysis in one direction vs. another. At these times, industry expertise can be used to apply business context and logic and steer the analysis. An applied data scientist has a sound understanding of his domain.
     
  • Puts Insight into Practise
    The capacity to offer practical solutions to business issues following the application of data science methods to the underlying data is what most distinguishes an applied data scientist. An applied data scientist brings the analysis to life by turning it into a story after experimenting with the data and contextualizing it with their domain expertise. A narrative that concludes with suggestions that the stakeholders can act upon based on the insights from the underlying data.
     
  • Simplify the Complex
    A layman may find some Data Science algorithms to be very difficult to comprehend. Simply put, math can be very difficult to understand, and this is especially true for those without a background in data science.
    People who make decisions are frequently the main stakeholders in data science projects, and they may or may not have a background in the field.

Role and Responsibilities⛑️

The Role and Responsibilities of a Data and Applied Scientist at Microsoft include the following:

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  • The Candidate is expected to be able to create, develop, evaluate, and fine-tune sophisticated algorithms and machine learning models that operate on enormous datasets, together with leading scientists and engineers from Microsoft's global R&D team.
     
  • The work is published in prestigious conferences while also generating strong company growth. Mixing cutting-edge research with immediate business impact.
     
  • The activities are pertinent to Microsoft's business line. They include things like upgrading Cortana, improving Bing's search and recommendation system, adding machine learning capabilities to Holo Lens, designing and improving Azure, and analyzing various critical business KPIs, among others.
     
  • Candidates with a solid theoretical grounding, the capacity to pick up new technical skills, and a dedication to designing, implementing and maintaining complicated machine learning models in large-scale systems.

Salary and Perks💸

The average annual pay for a Microsoft Data and Applied Scientist is a whopping ₹17,53,992. The annual salary range for data and applied scientists at Microsoft varies between ₹13,55,305 and ₹26,00,000.

 ** Above salary figures are subject to change

salary and perks image

It's always a good idea to include the perks of the job the company will provide, such as:

  • Health Care & Insurance
  • Performance Bonus
  • Maternity & Paternity Leave
  • Reduced or Flexible Hours
  • Educational resources
  • Free Lunch or Snacks
  • Opportunities to network and connect
  • Job Training & Tuition

Roadmap and Carrier map for the job🛣️

roadmap image

In order for you to become a Data and Applied Scientist, here are the things that should be at your fingertips: 

  • Strong Knowledge in Mathematics
    Math aptitude is crucial since it aids in our knowledge of the numerous machine learning algorithms that are crucial to Data science. Topics like Linear Algebra, Statistics, Analytic Geometry, Matrix, Vector Calculus, Probability, Optimization, Regression, Dimensionality Reduction, Density Estimation, etc.
     
  • Good Understanding of a Programming Language
    Programming principles like data structures and algorithms must be well-understood. PythonRJava, and Scala are the programming languages utilized in the industry. In some situations where efficiency is crucial, C++ can also be helpful due to its low-level architecture. Follow these problem lists and contests to improve your coding skills.
     
  • Well-versed with Machine learning
    Every year, new developments are made in machine learning (ML), one of the most critical components of data science and the most popular research topic. One must, at the very least, be familiar with the fundamental algorithms of supervised and unsupervised learning. Python and R have various libraries that can be used to implement these techniques.
     
  • Deep Learning is a must.
    TensorFlow and Keras are used in deep learning to construct and train neural networks using structured data. Topics like Artificial, Convolutional, and Recurrent Neural networks, etc. Understanding PyTorch is also important.
     
  • Natural language processing and Feature Engineering
    Learn how to improve your models the most efficient way with Feature Engineering. By developing your ability to work with text data, you may stand out in Natural Language Processing (NLP).

Check out Microsoft Interview Experience to learn about their hiring process.

Must Read Product Based Companies in India.

Resources🔨

As we have stated, there are many important career opportunities at Microsoft. In order to take advantage of these opportunities, preparation is essential, thus we are now giving you access to helpful materials.

We will watch a brief video on how to prepare for product-based companies before moving on to the resources.

We hope the video has taught you a lot. Please review the following resources from Coding Ninjas to get ready:

Frequently Asked Questions❓

At Microsoft, what does a data scientist do?

Data scientists use machine learning approaches to train, test, and deploy models to address business issues. Data scientists or Data and Applied scientists' job is to apply theoretical knowledge to actionable business solutions.

What does Microsoft mean by "Applied Scientist"?

The Applied Sciences Group works closely with Microsoft's product teams and other research divisions to push the envelope of technical viability. We create products and experiences that reach millions of people by applying fundamental ideas to real-world problems.

Are data scientists employed at Microsoft?

Due to its outstanding goods and services, Microsoft is a significant player and a top employer in the data science sector. One of Microsoft's divisions with the highest recruiting rates for data scientist positions is Azure, the company's cloud computing business.

What are data and applied scientists?

Simply described, an Data and Applied Scientist is someone who applies theoretical, conceptual frameworks and algorithms to the underlying data to offer actionable solutions to business challenges.

Do data scientists simply practice statistics?

In contrast to statisticians, data scientists are more interested in disciplines like computer science and machine learning. In comparison to statisticians, who are more concerned with the equations and mathematical models they employ for their analysis, they are also involved in developing and using data systems.

Conclusion✉️

This article covers everything you need to know about Data and Applied Scientists at Microsoft. Still have more questions; Here are some articles and courses for rescue:

You can also consider our online coding courses such as the Data Science Course to give your career an edge over others.

Do upvote our blog to help other ninjas grow.

Happy Learning!

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