Artificial intelligence went full steam ahead in Microsoft when it started with Bing in 2009. Later, the technology was extended to Azure, Edge, Cosmos and many others, making Microsoft a leader in Chatbots and Conversation as a Platform (CAAP) industry. There is no doubt that Microsoft is a game changer in whatever domain it enters. It not only creates long-lasting products but also takes over exciting projects.
Microsoft has been a major player in using and open sourcing data to build better products such as:
Cortana: According to Microsoft, there have been 12 billion questions asked on Cortana so far. With every question asked, the agent improves and understands the preferences and the world better. This platform is developed using natural language processing, speech synthesis techniques around which many machine learning models are built.
Virtual Assistants: Continuing Microsoft open-sourced approach toward Bot Framework SDK, the open source Custom Personal Assistant solution provides full control over the end user experience built on a set of foundational capabilities. They can now acquire information using reinforcement learning capabilities, a breakthrough that is going to improve these virtual assistants and forever change the customer experience.
For those who are interested in data science, there are quite a number of opportunities open at the tech giant’s India offices.
Data and Applied Scientist II
- PhD/MS in Computer science.
- Strong foundations in the mathematical aspects of learning including optimization, linear algebra & statistical modeling
- Hands-on experience in feature engineering and building scalable machine learning algorithms
- Python/ Perl/Ruby
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Data Scientist 2
- Masters/Engineering degree in computer science with specialization in Machine Learning, Statistics, applied mathematics or equivalent.
- Proficiency with statistical analysis tools line R, SAS etc.
- SQL, Python, .Net, C/C++, C# etc.
- Experience with big data technologies like COSMOS, Hadoop, and Cassandra etc.
- Exposure to database design and data modelling is a plus.
- Minimum 6+ years of relevant experience
- BS/MS degree in Computer Science or related quantitative field with 4-8 years of relevant experience
- Strong background in one or more of Machine Learning, Artificial Intelligence, Pattern Recognition, Natural Language
- Programming, Deep Learning, DNNs, Large-scale Data Mining
- Experience with scripting languages such as Perl, Python, PHP, and shell scripts
- Experience with recommendation systems, targeting systems, ranking systems or similar systems
- Hadoop/Hbase/Pig or Mapreduce/Bigtable or R/Matlab/AzureML or similar technologies.
Business Analytics Specialist
- Statistics – Basic probability distributions, estimation techniques, confidence intervals, hypothesis testing (z-test, t-test, ANOVA etc.).
- Machine Learning – Regression, Classification, Clustering, Data preparation (outlier treatment, missing value imputations etc.), Feature engineering (polynomial, log and other functional transformations), Feature selection, Time series, Text Mining & Model interpretation through Decision Trees, Linear Models
- SQL and experienced in working with RDBMS for data extraction and data read/write.
- COSMOS, Hadoop, MapReduce or any other Big Data technology.
- MS Power BI/Tableau
- Masters in Statistics/Econometrics or PhD in Statistics/Economics/ Computer Science or any MS with strong analytics knowledge.
Senior Security Data Scientist
- 6+ years of professional experience in experiment design and applied machine learning predicting outcomes in large-scale, complex datasets.
- Proficiency in R, Azure ML, Python, or other statistics/ML tools.
- Moderate coding skills along with SQL.
- A Masters or PhD degree with coursework in Statistics, Data Science, Experimentation Design, and Machine Learning highly desirable.
Here’s How To Prepare
Here is a list of sources recommended by the industry experts, that will help you meet the demands of the aforementioned job postings. You have access to all three mediums of learning. Choose your best option to crack your favorite job interview.
Read: This is a full text of the Python Data Science Handbook by Jake VanderPlas which introduces the reader to fundamental Python programming for Data Science
Listen: In this episode, Hugo anderson talks about the Python skills required to break into Data Science industry.
Read: This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it.
Listen: In this episode, Nathan Stephens, Director of sSolutionsEngineering at RStudio talks about the difference between statistics and data science, what an analytics administrator does, the future of R language and RStudio.
Read: This book breaks down into three primary sections: an introduction to the SQL Server R Services and SQL Server in general, a description and explanation of how a data scientist works in this new environment.
Listen: This podcast is intended for SQL Server database administrators, system engineers and developers with two or more years of experience, who are seeking to validate their skills and knowledge in writing queries.
Read: Elements of Statistical Learning by Trevor Hastie is the go-to book for beginners and experts alike. Every aspiring data scientist should possess a copy of it.
Listen: You can access subject specific podcast episodes hosted by industry experts.
Read: Ian Goodfellow and Yoshua Bengio combinedly contributed more to Deep Learning than rest of the community put together. In this book, they dissect most complicated topics and make it easy for beginners.
Listen: This podcast by Nvidia contains episodes with talks starring industry giants like Andrew Ng and Ian Goodfellow
Hadoop and Spark
Read: A short presentation by IBM about Hadoop and Spark that can be referred to before interviews
Listen: Managed by IBM, this gives the listener an introduction into the latest Big data technologies
Read: A very detailed read on the state-of-the-art data visualisations in Tableau
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