quantpsychguy

Hey folks - I am buying a house and want a bit of an outdoor kitchen. My question is about what type of fryer to get to go with it.

There is already a grill (plumbed with natural gas) in a brick surround. I was thinking about a table (or other flat surface) and then just putting a decent fryer out there.

Small fryer - just for fries or wings or chicken tenders (not planning on frying a turkey in it). Do I go with the el-genero counter fryer and just know it won't last super long?

Hey all, I have a question that feels different than what has been discussed here and I am hoping ya'll can help settle this.

My friend (who is Big4, I am T2, he says this is where the difference lies) feels he has been screwed over in his position. He is looking to change jobs.

My argument is to fake the job title - he is managing a consulting team, manages a delivery team (same client, different areas), and provides leadership to another consulting team on a different client. The delivery team is on a contract that he largely set up (I credit him with the BD here). That, to me, sounds like a Senior Manager.

His job title is Manager. He says if he puts SM on his resume, anyone in the con

Hey all, looking at a career transition out of the implementation side and into the sales / pre-sales side.

Many folks here who are in that space? If so I'd love to ask some questions and get the vibe. I have done quite a bit of the analytics side of DS and been involved in projects to do with Gen AI.

Is this type of thing best left to pure tech sales folks? One of the commonalities I hear is that pure tech sales folks really struggle to understand or sell in the AI/ML space. So I figured I'd look at moving over.

I've been tasked with finding out if we can do a GenAI based chatbot.

My general understanding:
- Take an input (which can be voice to text transcription for a customer service call center agent)
- Send that input, via API call, to a vendor (like Open AI or other ones, given the recent stuff maybe we look hard at other vendors)
- The API will respond with relevant information

Now this presumes that there is an LLM on the other end of that API call that knows the context of the conversation. If you want to have this work for your call center agents, for example, to help them figure out where to go next with troubleshooting, that LLM would need to be trained on your specific knowled

I am debating on going on my own to fill a niche my org is not interested in pursuing (small potatoes for them, a multi-billion $ org).

One of the things I am trying to figure out is tech side stuff. We'd ultimately have customer data in it and want to keep data secure and also available.

That brings us to a cloud solution I think. What have you done in your small orgs? AWS and GCP seem the most popular where I have worked to balance cost, security, availability, etc..

Any caveats I am overlooking?

Hey all, I work at a Fortune firm as a data science manager right now. I'm being tagged to take over management for a small data engineering team. It's not my background and I want to make sure I help these folks and don't screw everything up. :)

I've got management experience and understand the basics of programming and productionilization of code (though my experience is all DS side with python & R).

My question is what should I familiarize myself with to help set my new folks up for success? My experience with data engineering has largely been one of setting up ETL in automation for data processes but it's always been self service for my DS (and my DS team's) needs.

I know a lot of thi

Hey all, I have a question for the HR folks.

When you're asked for a resume and the ATS system formats everything incorrectly, I've always just deleted all but the most recent job and written 'see resume' at the top of my job duties.

I'm not sure if HR departments will look at that and scan the resume or, if I only have one job listed and 'see resume' in the job description, if they just auto-toss my application.

Is this a typical problem that a lot of ATS systems have that try to parse resumes?

Wanted to know group thoughts here.

Where I am, I do a big chunk of the ML implementation work, so think optimization models for customer pricing & targeting, stuff like that. Of course the modeling is not always the big problem - it's getting the data engineering to work and the business side to get along and the deployment to actually achieve the value that was estimated at the beginning. So not only the project management part but also KPI generation & scoping at the beginning and then going beyond the pilot to make sure it actually works and then on the glide path to success.

I want to get another gig where I am doing more of this. However I'm not sure what job titles to search

I know the entry level DS world is crazy right now but I'm thinking specifically about folks that have been DS for 3+ years.

Do most folks seem to work with DS that want to be ICs (individual contributors) at a high technical level or do most folks seem to want to be managers (i.e. have direct reports and administrative duties)?

In my anecdotal experience, I thought most that were non-junior DS wanted to stay in the more technical, hands on, IC side of the house. Am I wrong?

So a tech startup (one of the pre-IPO $1 billion+ valuation ones) reached out to me for interviews.

There are two parts that are suspect to me - first is they wouldn't give a salary band/range and recruiter said it was because of cost of living adjustments (i.e. lower pay if you live in midwest/southeast) and second is they have a 5 interview process that has a take home technical assignment.

I feel like this is bordering on a waste of time. I have heard nothing but horror stories about the technical assignment stuff and the BS about payband makes me leery.

Am I being unreasonable? Is it worth considering?

Do many of you work with folks that are billed as data scientists that can't...like...do much statistical analysis?

Where I work, I have some folks that report to me. I think they are great at what they do (I'm clearly biased).

I also work with teams that have 'data scientists' that don't have the foggiest clue about how to interpret any of the models they create, don't understand what models to pick, and seem to just beat their code against the data until a 'good' value comes out.

They talk about how their accuracies are great but their models don't outperform a constant model by 1 point (the datasets can be very unbalanced). This is a literal example. I've seen it more than once.

I can

Hoping for the $0.02 of the more experienced.

I work as a data science manager now with a group that's focused on predictive modeling and analytics. I've been reached out to by a recruiter for a Director role in a Business Intelligence group. The BI group is self-described 'immature' and the current work seems to be largely around turning reports into real time dashboarding. They also want to move into "ML and AI" which is clearly not a short term thing and I'm confident they don't even understand what that means.

Is it a terrible idea to take a step from a data science manager and move into a group that is doing mostly BI work and trying to build out their predictive function? Or is it th

Odd question for the lot - it seems like you hear about folks being hit up on linkedin for positions (by recruiters and the like). I got kinda sick of getting everyone who wanted a tech PM reach out to me so I removed some of the more obvious PM stuff. It's just donned on me though that no one has pinged me about data science roles.

Is that pretty normal? Do folks not tend to get bombarded with 'requests to chat' from recruiters & headhunters about data science roles? Kinda makes me wonder if I've done something wrong on my profile at this point.

I don't actually want to change roles right now. I'm mostly just curious.

I'm curious as to this group's thoughts on the types of tools that 'democratize' data science.

I'm thinking specifically about the types of tools that are supposed to allow for citizen data scientists to do the work of modeling - tools like H2O's Driverless AI, DataRobot, Pecan, and/or RapidMiner. Is this like democratization of web development (which turned into anyone can make a website and really all it did was make the simple stuff easier) or does this fundamentally change the nature of what data scientists do in 5+ years?

I'm not talking about stuff like Google's BQML or python toolskits/libraries - those really seem like they can make a good data scientist a bit better at modeling bu

I've been asked (with short lead time) what continuing education/growth we should be providing for our team for next year. Since the pandemic started I've largely stopped even thinking about conferences or the like related to data science (specifically on the side of analytics).

What conferences or other continuing education do you think would be valuable to suggest to leadership to grow our practice (specifically, to grow the expertise of my employees, mostly data scientists, analysts, and engineers)?

I'm going to ask them as well but was hoping for ideas from folks spread out across the industry.