Why did you leave (or are leaving) [company]?
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217
Machine Learning / AI
3611
0
How did you arrive at a specific confidence score threshold?
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191
Are you currently working or not?
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181
Was there any additional training of the model for this task (not LLM)?
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174
Is everything clear to you about the product (media plans), or would you like to clarify something?
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174
How would you now (with current knowledge) solve the classification and routing of requests in a service desk, using a multi-level approach rule-based → ML → LLM fallback → human? Name specific technologies, models, and metrics.
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162
Were you the lead in Data Science in your team at [company]? What approaches and technologies did you originate, and which did the tech lead define?
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154
Compare linear and nonlinear models in classical ML — pros and cons.
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152
What types of tokenization do you know? Pros and cons of each.
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151
Briefly tell me about the team you worked in at [company].
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151
Tell about regularization: what it is, why it is needed, what types exist.
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144
Was it a test/pilot or did it go directly to production and was used by managers?.
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144
What was the product at [company] from the user's perspective (document flow, data extraction from KS-2/KS-3/estimate)?
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141
What is a gradient from a mathematical point of view and how is it applied in machine learning?
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138
Tell me about a classic ML project (not LLM) that you are proud of.
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130
How did you understand whether the model classified the data correctly? What actions were required from the manager?
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118
Tell about yourself, your experience, and key projects (especially about the corporate GPT system at [company]).
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118
How did you measure data extraction quality?
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114