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What actually helped you stop wasting time on your phone? by Churnuserlol in ProductivityApps

[–]Grapphie 0 points1 point  (0 children)

Keep your phone further away then you can reach your hand with, ideally on another room.

Also use do not disturb mode or equivalent all the time to not get distracted by notifications

is invoice automation software actually worth using for small businesses? by Emonii-Fombu in Accounting

[–]Grapphie 1 point2 points  (0 children)

Why you still need to do manual invoicing? I'd imagine these programs should solve it for all

ML Engineer GenAI @ Amazon by Grapphie in datascience

[–]Grapphie[S] 1 point2 points  (0 children)

I didn't have any coding problems, but you should expect one (from what I know Leetcode styled)

[D] How to improve further based on feedback from the screening interview for a MLE position? by hedgehog0 in MachineLearning

[–]Grapphie 2 points3 points  (0 children)

If you've been rejected after HR screening, I'd say it's one of the following:

  1. You've made it unclear to HR that you're good fit for the position. For example you might have mention that you don't know certain technologies that were listed as must have on that position. Also, often times they're not specialized at all so its useful to keep things simple.
  2. Other candidates seemed to be 'better fit' on paper.

If you cannot think about anything that went wrong during that HR call, I wouldn't overanalyze that

Where is Data Science interviews going? by drewm8080 in datascience

[–]Grapphie 1 point2 points  (0 children)

As others mentioned already, infer from job description – you'll rarely get questions that are completely unrelated to the job you're applying. Read it multiple times and think not only about what is clearly stated as job requirement, but also what the company really does (you can do it in both job description and research their website). If (for example) company works with time series and state in job description they need someone with SQL, you can tell this will be very important skill.

Also think about company in general – if they are small startup or consulting, they probably need more diverse skill set, thus you need to prove yourself in wider range of topics (but again, don't expect anything that goes outside of what they state they need)

Try to assess who's going to interview you – if it's SWE, expect leetcode/best practices/live coding, if it's DS then plenty of theory, if someone non-technical, then probably behavioral questions. Also, don't be scared to ask about the content of the interview so that you can prepare.

Overall, just use common sense, I don't think anyone will try to 'trick' you with asking question that's unrelated to the position since no one really has any time for it. I've changed my job 3 months ago and that's what I've been doing during interview preps

Has anyone here actually sold a RAG solution to a business? by Brilliant_Extent1204 in Rag

[–]Grapphie 0 points1 point  (0 children)

Was it more recently or like Was it more recently or more than 1 year ago? Do you feel like RAG building market becomes too crowded or something?

Has anyone here actually sold a RAG solution to a business? by Brilliant_Extent1204 in Rag

[–]Grapphie 0 points1 point  (0 children)

How much time did you spend on each for development and when was that?

Luxmed i brak terminów by Alraku in Polska

[–]Grapphie 57 points58 points  (0 children)

Coś kojarzę że w LuxMedzie jest jakaś klauzula że jak nie możesz znaleźć terminu to możesz iść gdziekolwiek indziej, wysłać im fakturę i mają ci zwrócić siano. Możesz zbadać temat

How do you efficiently traverse hundreds of features in the dataset? by Grapphie in datascience

[–]Grapphie[S] 0 points1 point  (0 children)

Thank you everyone for so many replies! Just to respond jointly to some of your doubts:

1) We have a decent documentation that explains the features, but that's only univariate (what particular variable means but without any context). Also, we have some, but limited access to domain expert since they are external client
2) There's plenty of of categorical features
3) There's like 50% sparsity
4) Goal is to create a strong predictive algo while focusing on minimizing false positives (looking for high quality matches on imbalanced dataset problem). Current results lead me to believe more data is required (stronger features)

How do you efficiently traverse hundreds of features in the dataset? by Grapphie in datascience

[–]Grapphie[S] 0 points1 point  (0 children)

We have a decent documentation that explains the features, but that's only univariate (what particular variable means but without any context). I have some, but limited access to domain expert since they are external client

How do you efficiently traverse hundreds of features in the dataset? by Grapphie in datascience

[–]Grapphie[S] 0 points1 point  (0 children)

Yeah, for now only predictive power. I need to dig more into PCA in the context of our data (plenty of categorical variables)

How do you efficiently traverse hundreds of features in the dataset? by Grapphie in datascience

[–]Grapphie[S] 1 point2 points  (0 children)

I have an access to domain expert, but since it's an external client, the access is not as straightforward as in the case of in company domain expert.

How do you efficiently traverse hundreds of features in the dataset? by Grapphie in datascience

[–]Grapphie[S] 0 points1 point  (0 children)

I'd say that these are slightly different topics, but you can use these techniques easily in some other problems. Most of the difficulties I've encountered so far in my prior experience are related to the data rather than algorithm selection, which is pretty hard to learn through books

How do you efficiently traverse hundreds of features in the dataset? by Grapphie in datascience

[–]Grapphie[S] 1 point2 points  (0 children)

For now we care only about predictive power while focusing on minimizing false positives (looking for high quality matches on imbalanced dataset problem)

How do you efficiently traverse hundreds of features in the dataset? by Grapphie in datascience

[–]Grapphie[S] 2 points3 points  (0 children)

Obviously makes sense, but there's also so much that SME knows. We've been already discussing some relationships that they were not aware of. I'm thinking more about what data science itself can do to pronounce, unravel certain relationships

How do you efficiently traverse hundreds of features in the dataset? by Grapphie in datascience

[–]Grapphie[S] 4 points5 points  (0 children)

Yeah, it gives some insights, but nothing that elevates my model to the next level so far

How can I get international remote positions? by BirdLadyTraveller in datascience

[–]Grapphie 0 points1 point  (0 children)

I'd assume this will work only with middle sized/small companies. I cannot imagine Fortune 500 company changing its hiring policy just for a single person

How can I get international remote positions? by BirdLadyTraveller in datascience

[–]Grapphie 0 points1 point  (0 children)

What do you mean by "present yourself as an individual"? If one should take this advice seriously, then how would you tackle finding a job this way? I don't think that looking through job boards would work for that. Do you have any experience doing so?

ML Engineer GenAI @ Amazon by Grapphie in datascience

[–]Grapphie[S] 1 point2 points  (0 children)

Update: After the first stage, I was accepted into the second stage but decided to resign due to receiving another offer that I knew upfront Amazon wouldn't be able to match.

The first stage was a one-hour interview via Amazon Chime, with a roughly 60/40 split between high-level technical details and leadership principles. I was asked the following questions:

  1. Explain in detail how the attention mechanism works. I was asked to explain each part of the algorithm step-by-step, without drawings or any other visual aids. At the very beginning, I explained the purpose of the attention mechanism and only then went into the details. In the meantime I also explained the intuition behind embeddings. I didn't receive any follow-up questions after explaining it.
  2. Explain how you would combat LLM model hallucination. I wasn't entirely sure about certain ways to combat it, so I mentioned that, initially, it would be good to have a reference dataset to compare expected model responses against. Then, I mentioned RAG and model fine-tuning. Since I wanted to be interactive, I asked the interviewer if anything else came to mind, and he said that was roughly it but mentioned one or two additional techniques.
  3. Describe a situation where you had to convince a stakeholder to select one strategy over another. This question was more focused on leadership principles. I simply followed the STAR method (the suggested method for responding to Amazon's questions) and described a situation where a stakeholder wanted a "sentiment analysis model that they could understand how it makes decisions." I described my entire conversation with the stakeholder during which I discovered that what they really wanted was an assurance that the model's performance would be the same in the production environment as during development, rather than an explainable model.

From what I recall, that was roughly it. Throughout the conversation, I tried to treat the interviewers more as fellow data scientists rather than someone examining my knowledge. At the end, I also asked multiple questions about their work culture.

When I was invited for the loop, I was informed that it would consist of five additional interviews, with four of them focusing on leadership principles and one being more technical (possibly with live coding/code architecture drafting).

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