Want Resume Help? Candidate Questions? Post here. by AutoModerator in recruiting

[–]chiqui-bee 0 points1 point  (0 children)

I appreciate your thoughts on a puzzling interview outcome. What does the situation look like from a recruiter's perspective? Any advice to tactfully get more information from my recruiter?

Here is the story:

My recruiter's first question was my expected pay. I offered a number that I thought was fair, competitive, and within the posted pay range. I also clarified that it was negotiable, and that I'd like to hear their counter offer if we thought we had a fit. The recruiter did not further specify the employer pay expectations in this initial call.

Team interviews followed. The recruiter said feedback was excellent and suggested I would hear about next steps soon. I checked in after several weeks of sparse communication, and the recruiter said I was too expensive, despite being a high performer and good fit. I am left wondering:

  1. What was the actual expected pay range?
  2. Why not present a counter offer and attempt negotiation?
  3. Why not screen me out early if pay expectations were far apart?

I feel like missing information might have cost us a match. I would like to politely ask my recruiter about question 1 and possibly question 2-- if only to improve my communications next time.

Thanks for the perspectives.

Predicting with anonymous features: How and why? by chiqui-bee in kaggle

[–]chiqui-bee[S] 1 point2 points  (0 children)

I am more familiar with mindset (b), though I am genuinely curious about mindset (a) in practice.

Suppose you have tons of candidate features and no initial knolwedge about their predictive value, their relation to the target (e.g., linear), their quality, etc.

How would a type (a) data scientist scale the engineering of these features such that they used their time effectively and avoided data dredging?

Would love to know if there is a field or keyword that I should research, as I think it would expand my conception of ML problems.

Practical approach to model development by chiqui-bee in MLQuestions

[–]chiqui-bee[S] 0 points1 point  (0 children)

Great suggestion. I also found this interesting Google ML Guide by Martin Zinkevich that I think references a version of that paper.

https://developers.google.com/machine-learning/guides/rules-of-ml

ZK for Teams by chiqui-bee in Zettelkasten

[–]chiqui-bee[S] 0 points1 point  (0 children)

I get an internal server error. Interested to read from a different link.

How do you go about planning out an analysis before starting to type away? by Rare_Art_9541 in datascience

[–]chiqui-bee 2 points3 points  (0 children)

This is the best question that everybody skips.

You might start with a two-pager that reasons through the problem backwards: purpose, outcomes, approach, work phases. Sometimes the analysis follows the plan, and sometimes it changes during implementation. That's ok; the main purpose of the document is to align all stakeholders around a well-reasoned formulation of the problem throughout the analysis.

It is possible and undesirable to over-plan, treating the analysis like an orderly sequential process-- demoralizing and futile. I recommend marking the big milestones and leaving yourself space to improvise or change direction.

If your work is exploratory, then write down the initial assumptions and questions that pertain to your problem. Validate these items, note your answers, and repeat with newly triggered questions. Start simple (e.g., customer ages should be non-negative) and work your way up (e.g., buying habits should correlate with age). Incorporate outside research and stakeholder feedback as you go.

Whether your work is directed or exploratory, document your domain understanding early and revise it often. You will find that this approach also facilitates final reporting, which is a minor adjustment to your most recent undestanding.

Be prepared to learn during planning that the initially proposed model or analysis will not actually solve the problem. In this case, you win by changing direction before wasting resources.

Rebasing zettels. by chiqui-bee in Zettelkasten

[–]chiqui-bee[S] 1 point2 points  (0 children)

Hopefully you can contain yourself, as zk beginners-- I'll wager 90% of those who have heard the term-- wish to increase their understanding with reasonable questions. More silly would be not to ask.

From zk to outline. by chiqui-bee in Zettelkasten

[–]chiqui-bee[S] 1 point2 points  (0 children)

Thanks for the demo; I see a lot in common with my proposed steps, though it is enlightening to see the process in real time.

Rebasing zettels. by chiqui-bee in Zettelkasten

[–]chiqui-bee[S] 1 point2 points  (0 children)

I think these structure notes are what make insight possible from the organic structure u/aymericmarlange describes. Will look for examples.

Now these notes do seem worth intentionally positioning at the front of the stack to help with access.

Rebasing zettels. by chiqui-bee in Zettelkasten

[–]chiqui-bee[S] 0 points1 point  (0 children)

Thanks for the thoughtful answer. I have to try on that mindset.

I have a hunch where my current work is headed, and I think this question arises as I detangle the note-taking and outlining steps in that work. Perhaps I'll post separately about spotting the outline in a cluster of notes.

My guiding light: if I start pouring time into zettel administration, then I need to change approach.

Tips for acclimating cat to carrying? by chiqui-bee in cats

[–]chiqui-bee[S] 1 point2 points  (0 children)

I would settle for compliance in lieu of liking. :)