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Amazon Jobs in Canada 2022, Data Scientist Amazon Job, Data Scientist Job Salary, for Foreigners, for Indians Graduate, We are looking to hire a highly creative data scientist to address data analytics challenges in our organization, to collect large volumes of data from varying sources, clean and interpret data, create solutions to overcome challenges, and communicate with interested parties.

Data scientists analyze raw data by using statistical and mathematical techniques, and computing. This may include filtering and processing data to reach meaningful interpretations and conclusions, and creating solutions.

To succeed in this position; you need to be curious, creative, and tech-savvy. You need to stay up to date with data programming software and apps, have an outstanding understanding of statistics and mathematics, and be proficient in writing algorithms. Top candidates will be persistent and have excellent analytical and problem-solving skills.

Post Name Data Scientist, Advertiser Controls
Company Name Amazon
Expected Salary CAD 75000 To CAD 80000 Per Year
Address Toronto, Ontario, Canada M5H 4A9

Responsibility

  • Solve real-world problems by getting and analyzing large amounts of data, diving deep to identify business insights and opportunities, designing simulations and experiments, developing statistical and ML models by tailoring to business needs, and collaborating with Scientists, Engineers, BIEs, and Product Managers.
  • Write code (Python, R, Scala, SQL, etc.) to obtain, manipulate, and analyze data.
  • Apply statistical and machine learning knowledge to specific business problems and data.
  • Build decision-making models and propose solutions for the business problem you define.
  • Retrieve, synthesize, and present critical data in a format that is immediately useful for answering specific questions or improving system performance.
  • Analyze historical data to identify trends and support optimal decision-making.
  • Formalize assumptions about how our systems are expected to work, create the statistical definition of the outlier, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions are needed.
  • Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes.
  • Conduct is written and verbal presentations to share insights to audiences of varying levels of technical sophistication.
Industry Private
Schedule Type Full Time
Education Required Bachelor’s Degree in Computer Science
Remote No

Basic Qualification & Skills

  • Bachelor’s Degree.
  • 3+ years of experience with data scripting languages (e.g SQL, Python, R, etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab).
  • 2 years working as a Data Scientist.
  • Experience in as many of the following areas: causal inferencing, multi-variate testing & design, A/B testing & design, descriptive analytics, and regression analysis.
  • Good understanding of supervised and unsupervised learning models.

Preferred Qualification & Skills

  • Advanced degree in Computer Science, Mathematics, Statistics, Economics, or related quantitative field.
  • Broad knowledge of ML methods, statistical analysis, and problem-solving skills.
  • Expert level knowledge in statistics and sophisticated user of statistical tools.
  • Experience in data applications using large-scale distributed systems (e.g. EMR, Spark, Elasticsearch, Hadoop, Pig, and Hive).
  • Experience processing, filtering, and presenting large data sets (hundreds of millions/billions of rows).
  • Combination of deep technical skills and business sense, to interface with all levels and disciplines within our customer’s organization.
  • Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment.
  • Excellent verbal and written communication skills with the ability to advocate technical solutions for science, engineering, and business audiences.
  • Ability to develop experimental and analytical plans for data modeling, use effective baselines, and accurately determine cause-and-effect relations.
  • Experience in computational advertising is a plus.

About Company

Amazon.com, Inc. is an American multinational technology company that focuses on e-commerce, cloud computing, digital streaming, and artificial intelligence. It has been referred to as “one of the most influential economic and cultural forces in the world”, and is one of the world’s most valuable brands.

Amazon was founded by Jeff Bezos from his garage in Bellevue, Washington, Initially an online marketplace for books, it has expanded into a multitude of product categories: a strategy that has earned it the moniker The Everything StoreIt is one of the Big Five American information technology companies, alongside Alphabet, Apple, Meta, and Microsoft.

Amazon’s mission statement is: “To serve consumers through online and physical stores and focus on selection, price, and convenience.”
 
Amazon Vision Statement
Our vision is to be earth’s most customer-centric company; to build a place where people can come to find and discover anything they might want to buy online.

Interview Questions for Data Scientists:

  1. What is Data Science?
  2. Differentiate between Data Analytics and Data Science
  3. What do you understand about linear regression?
  4. What do you understand by logistic regression?
  5. What is a confusion matrix?
  6. What do you understand by true-positive rate and false-positive rate?
  7. How is Data Science different from traditional application programming?
  8. Explain the difference between Supervised and Unsupervised Learning.
  9. What is the difference between the long format data and wide format data?
  10. Mention some techniques used for sampling. What is the main advantage of sampling?
  11. What is bias in Data Science?
  12. What are the popular libraries used in Data Science?
  13. What is variance in Data Science?
  14. What is entropy in a decision tree algorithm?
  15. What information is gained in a decision tree algorithm?
  16. Explain how a recommender system works.
  17. What is a normal distribution?
  18. What is an RNN (recurrent neural network)?
  19. Explain selection bias.
  20. What do you understand by a decision tree?
  21. What do you understand by a random forest model?

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