1. Describe the bias variance tradeoff. 2. How would you measure the performance of a classifier? 3. What is an example of a problem where you would favor recall over precision and vice versa.? 4. Describe the main idea behind boosting along with some well known algorithms such as adaboost and xgboost. 5. Describe several options for embedding text data into a feature along with pros and cons of each. 6. Describe your favorite sorting algorithm
Projects Contribution in each project Guesstimates Knowledge on ML concepts and application
This is out of context, but one question I wrote down was, "What would you tell a CIO to ask a vendor when evaluating an off-the-shelf AI product?"
How do you plan an L&D program?
Tell me about your experience
One interview was basically a Machine learning course exam, asking about what I know about the basics of ML and metrics and so on. The seconed one was mostly detailed on my projects
* Ask some basic questions related to the position * Introduce some old projects related to this position, why you want to change your job
Initial screening was about my projects, future goals and motivations. Assignment was regarding problem with mnist.
Pros and cons of different generative models (e.g. graph diffusion, text-based transformers etc.) used in the biopharmaceutical industry. Ways to improve model throughput.
Tell me about yourself & your career, interests, why Novo? Expected salary.
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