IBM Data Science Interview
In oncology, IBM Watson is at work supporting cancer care in more than 230 hospitals and health organizations.
IBM Watson is an AI assistant for business. Watson Assistant has been named the leader in Forrester’s New Wave report on Conversational Computing. IBM provides a stack, models, solutions and Apps for all things AI. They are a full stack AI company.
Interview Process
IBM Data Science team uses HireVue to screen candidates. The challenges happen on the Hirevue platform setup by the IBM Data Science team. There is a mix of questions which require short write ups, free form explanation of concepts, video responses and coding solutions. That is followed by onsite technical and behavioural interviews. The on site sessions are with people from Information Design, Statistics and Machine Learning as well as Management.
Important Reading
- IBM Watson Assistant Docs: Watson Assistant
- Building Business Chatbots: Chatbot tutorials
- Watson Discovery: Introducing Watson Discovery for Salesforce: An AI-powered insight engine for CRM
AI/Data Science Related Questions
- Explain P-Value? What is the importance for P-Value?
- How do you deal with missing value in a data set?
- Difference between supervised & unsupervised learning? Provide examples.
- How do you evaluate the performance of a regression prediction model vs a classification prediction model?
- Describe Precision and Recall.
- What is the matrix used to evaluate the predictive model?
- What are the relationships between the coefficient in the logistic regression and the odds ratio?
- How do you validate a machine learning model?
- What is a confidence interval?
- What is specificity? What is sensitivity/recall?
- What is the most important aspect in CRISP-DM?
- Given a subset of daily sales and sellers, find the subset that identifies those with the highest daily sales average.
- For a particular case study, why did you use Monte Carlo to help with stress testing an algorithm?
- What is Tensorflow?
- How do you parse JSON Strings in Python?
Reflecting on the Questions
IBM Watson has been around for a long time. The interview process is very rigorous as it tests multiple aspects of data science. Fundamentals and coding are very important. Interviews also focus on projects based on the candidates resume and drilling deep on decisions made during those projects and the rationale behind them. Serious hard work is required to ace these interviews.
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The sole motivation of this blog article is to learn about IBM AI and its 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.
