Ebay Data Science Interview
Ebay hired Jan Pederson, previously VP of Data Science at Twitter to lead the Artificial Intelligence efforts within the company.
Jan Pedersen is credited with numerous patents, having worked at Xerox’s Palo Alto Research Center, AltaVista, Yahoo, and A9. He is truly the AI leader who has the power to transform the AI efforts at eBay. Currently, eBay lists jobs all over the world in their Data Science team on their careers website making the team a truly global one.
eBay recently acquired Expertmaker — a company that has created an advanced AI platform enabling optimization and automation.They already use machine learning algorithms to recognize objects in listings, find similar products and rank recommendations. They deploy AI in various areas, from structured data to machine translation to risk and fraud management.
At Acing AI, the aim is to help you to get into Data Science and AI. I have profiled some of the best technology companies and written articles about AI interviews at Microsoft, Google, Amazon, LinkedIn, Ebay, Twitter, Walmart, Apple, Facebook, Zillow, Salesforce, Uber, Intel, Adobe Tesla and most recently IBM. This has led to being the top writer in Artificial Intelligence on Medium. The AI interview preparation guides Part 1, Part 2 go over the details which help you ace any AI interview. Acing AI Portfolios helps you to showcase your AI work. Expert interviews and analyses gives you a sneak peak into the lives of AI/Data Science Leaders and analyses of AI tech companies. Now onto the eBay Data Science Questions article…
Interview Process
eBay Interview process is pretty straight forward. There is a phone screen followed by an onsite interview. There are four to five consecutive interviews onsite. These interviews consists of past experiences and project case studies, technical interviews on statistical and SQL knowledge.
Important Reading
- Introducing Krylov: AI Platform that Empowers eBay Data Science and Engineering Teams: GPU Technology Conference 2018
- Innovations in Recommender Systems for a Semi-structured Marketplace: eBay: Yuri M. Brovman, Data Scientist, eBay, NYC 2017
- Data Science/ML Archive: eBay Blog Archive
AI/Data Science Related Questions
- How could you improve the accuracy for customer query?
- Suggest a modelling process for a binary classification task with skewed and unbalanced data.
- You observed two gambling machines for a period of time: one of them occasionally give rewards; the other one never give any rewards. Which one do you pick to play with?
- Build a model to identify customers interested in receiving ad emails.
- Case study: eBay has to identify the cameras from the other junk like tripods, cables and batteries what is the approach? Data looks like Title, description of the product, Price, Image,…etc
- Given a function roll() that uniformly returns a double between 0 and 1 and a array/list of numbers of length N (no duplicates), create a function shuffle() that returns a permutation of equal probability
- In linux, how to search a string in a Gigabytes file and print that string in the terminal.
- What is the total length of all the roads in San Francisco?
- Explain PCA, SVM, Backpropagation, Hidden Markov Models and Viterbi Algorithm.
- Explain boosting, bagging and stacking.
- What is CRF?
- What is Naive Bayes?
- What is a language model?
- Estimate the average number of transactions on eBay per day.
- There is a file of size 100 gb and you have given a memory of size 100GB. How you will sort the file on basis of ASCII. File is having line of text inside it. No line in the file is repeated.
Reflecting on the Questions
eBay has a long history of Data Science in the company. The team was not created as a result of the current hype but has been around for almost a decade. The questions reflect this maturity. There are also some questions which are brain teasers or puzzles. Based on their blogs, they use Hadoop, Spark and TensorFlow for Deep Learning.
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The sole motivation of this blog article is to learn about EBay and its AI technologies helping people to get into it. All data is sourced from online public sources. I aim to make this a living document, so any updates and suggested changes can always be included. Please provide relevant feedback.
