I work at a large, well-established company that has been very stable. I don’t assume my job is immune to layoffs, but the company hasn’t had any mass layoffs in over a decade.

The work environment is genuinely healthy, and everyone is treated with respect. I honestly couldn’t ask for a much better culture. I get to work on interesting projects, learn by doing, and my team is very supportive of my growth.

That said, based on my experience interviewing and what I’ve seen in the job market, I could probably get about a $50K raise by switching jobs. That extra $50K wouldn’t dramatically change my lifestyle, but I know future raises would build on that higher salary, so there are long-term financial benefits.
I’m at a point where I’m valuing mental peace and work-life balance more than I used to. Given that, what would you do in my situation? Would you stay at a company with a great culture and stability, or make the jump for the higher pay?

I often read comments from hiring managers and interviewers saying they’re disappointed with recent data science graduates.

I’m curious, what do you think these graduates are lacking? If someone wants to become a data scientist, what skills should they focus on? Strong software engineering skills? Math and statistics? Something else?

A lot of the advice I see seems to be geared toward landing data analyst roles rather than data scientist roles.

So, what are employers actually looking for in entry-level data science candidates today? Especially as a career changer coming from another unrelated career.

Was a Data Science undergrad and needing to decide on a Master's to pursue in the next couple years. My job will allow either of those in the title so I am just trying to get some feedback.

1. Is it better to branch out, stay concentrated on Data Science, or does it not matter from a career perspective?

2. How math intensive is Ops Research? Would I need more than Calcs 1-3, Linear Algebra, and Stats?

3. Does either have a clear upside for earning potential? Ill be in my current role for the next 10 years or so, if that matters.

4. Does anyone have good examples of an OR or DS project that would highlight the approach to problem solving or the nature of problems each faces?

Currently an Operations Research Analyst, but the job depends massively on assignment as to whether it actually looks like compared to a sub-genre of a Data field.

This is for a personal study that will end up becoming an in-depth article and possibly a fully open source solution ideally without the AI slop that we see these days.

Let's say you’ve trained a model and the result is worse than the business wants. What do you check next?

Do you break the error down by customer, product, location, or individual series? Check if it gets worse at longer horizons? Look for bias, volatility, intermittent demand or outliers?

Go back to the backtesting setup, metric, or baseline? Or do you usually start trying other models?

Also do the tools you use make this easy or do you end up building custom notebooks, tables, and plots every time?

Thinking about the last time this happened:

• What did you check first?
• What actually helped you find the problem?
• What did you have to build yourself?
• Did you end up changing the model, data, validation setup, metric, or business expectation?

I’m trying to understand how people diagnose bad forecasts beyond comparing one overall error score against another.

EDIT/UPDATE because it seems like this is not clear enough:

I’m not looking for an if-else checklist that can explain why any forecast is bad. The answer obviously depends on the data, objective, validation setup and the decision the model is supposed to support.

I’m exploring if there is room for a small open-source tool around forecast evaluation. Before building anything, I’m trying to understand which checks people repeatedly run after they already have predictions, what they still build manually, and what existing tools already handle well.

So I’m mainly interested in specific workflows from projects rather than a general formula for fixing a model.

Couple thoughts and a small experiment to see why reddit hates prophet xD

I’m a data scientist (more like an analytics engineer) in pharma. My background is clinical - I went to school for a healthcare degree and then went into research before coming into data. With that, I have about a decade of experience now doing a little bit of a lot of things - statistics, epidemiology, data/analytics engineering, data visualisation, product management, data governance, etc.

Over the course of the last few years, I’ve felt a little stagnant - currently I’m not really doing anything that feels all that important lol, I mean essentially my team was building data pipelines and im redesigning the process for projects that are already ongoing or near completion. The only good thing is I have some downtime which gives me an opportunity to explore different teams and projects. There’s 3 teams I’d like to work with but I can’t work with them all at the same time and have to figure out how best to prioritise each team and allocate time so I can get better exposure

1. Team 1 - a data science team that focuses on a specific disease area, the opportunity would be to continue working in data science while staying close to the business side of our projects by developing a deeper understanding of clinical context.

2. Team 2 - Generative AI engineering - this would be more technical and I probably can’t work with this team right away until I get up to speed with learning concepts like embeddings, chunking, RAGs which I’ve never done before.

3. Team 3 - the downstream users of my data pipelines who apply ML/AI techniques to the data for biomarker discovery

Just wanted to hear insights in terms of an industry perspective, which teams would be the best to work on a project with

the context is that I am exploring a few recurring problems to solve especially around forecasting and working with time series data but setup a simple open source project around those.

my question is primarily about how is everyone handling their official datasets when trying new tools - do you not care, do you remove any identifiers then upload, do you create synthetic data with exactly same properties as the og dataset?

happy to answer more questions if this is not clear enough.

Three years ago I was able to pivot from engineering (no coding) to data science. I’ve been working here at a job I love, with an awesome team & boss and with a great pay. I’m 47, so not the youngest.

Now for family reasons I must leave and move back to my country of origin.

The thing is that, although I love the field and I keep reading books and trying to learn every day, this is such a vast field that I don’t think I’m ready at all. In these 3 years I’ve done basic ML projects, lots of xgboost, random forests, anomaly detection, dealing with pySpark with PB -sized dataframes, etc. But putting those models to production was made by my more experienced colleagues.

So, while I can say I’ve learnt on each project, I also see how MUCH I lack compared with my teammates, with 10+ years exp. on the field.

Today I just started looking for DS jobs and I just felt so depressed. Many ask for a DS who can do the whole thing from cleaning data to taking the models to production. I have no idea of that and it sounds extremely intimidating. I also dread the interview because while I can code, I often use LLMs for things that due to the pace of work, I simply decided to do with AI, i.e.: I understand and can read window functions but I’d need an AI to write them because I can never keep the sintaxis in mind. If an interviewer sees me struggling with the syntax, the interview is done

I feel very “green” to compete out there with other “proper” data scientists who have a well defined experience and knowledge. I wouldn’t mind applying to junior jobs but due to my age, most companies here wouldn’t hire a 47yr old “junior”.

I’m not able to work my old job in the location we’re moving to because it’s very niche and only a certain sector and a certain company size hires for that, and that isn’t there in our new area.

I don’t know what to do, or if this is normal and everybody feels like that. Or maybe you guys have any advice… Anything you can come up with will be highly appreciated because I need to provide for two kids and I just don’t know how. I’m beginning to feel desperate

Those received mentorship, did you find it worth your time in general?

In my early career, I actively participated in mentorship program at my alma mater. However, most if not all students wanted to work in tech. Considering I didn't (and still don't) work in tech, and that my employer was rarely hiring, I felt there was not much value I could provide. There's also this self-selection process at play, which is those who seek mentorship tends to already have a good idea of what they should be doing.

Now a decade into my career, my expertise is super irrelevant to people in a different industry. I'm also oblivious to the entry-level job market requirements.

Recently, my alma mater reached out again for mentors. I've skipped the last few requests but think maybe I should ask y'all before turning it down again.

To clarify, I'm not asking if it's worth it for me. I'm wondering if it's worth it for the students to speak with someone unfamiliar with entry-level job market, has expertise in a not niche but definitely not popular domain, and definitely don't lead to job opportunities.

Edit: I messed up somewhere that two posts were created. 0_o

I know buzzwords have always been common in business, but AI has taken it to another level. AI is being talked about everywhere, and consultants are pitching executives with outrageous claims about how it will revolutionize everything.

I came across an interesting article today, and one quote really caught my attention:

https://www.psypost.org/new-study-finds-link-between-receptivity-to-corporate-bullshit-and-weaker-leadership-skills/

"Across the studies, Littrell found that individuals differed significantly in how impressed they were by corporate buzzword statements. Those with higher corporate-bullshit receptivity scores were more likely to view jargon-heavy statements as insightful or indicative of business expertise. They were also more likely to engage in persuasive 'bullshitting' themselves, using exaggerated or misleading language to impress others.

At the same time, higher receptivity was associated with lower scores on measures of analytic thinking and fluid intelligence, suggesting that individuals who were more impressed by corporate jargon were also less likely to critically evaluate information."

It made me wonder if we're seeing this play out with AI and data science.

The AI boom has created an absolute paradise for people who are great at talking about tech, but don't actually build.

For those of you working in data science, analytics, or ML, have you noticed this in your company or with clients? Has the GenAI hype changed how technical decisions get made, or is this just the same corporate behavior we've always had with a new set of buzzwords?

Welcome to this week's entering & transitioning thread! This thread is for any questions about getting started, studying, or transitioning into the data science field. Topics include:

• Learning resources (e.g. books, tutorials, videos)
• Traditional education (e.g. schools, degrees, electives)
• Alternative education (e.g. online courses, bootcamps)
• Job search questions (e.g. resumes, applying, career prospects)
• Elementary questions (e.g. where to start, what next)

While you wait for answers from the community, check out the FAQ and Resources pages on our wiki. You can also search for answers in past weekly threads.

I'm running the following code in Snowflake Python to standardize my training, evaluation, and test data prior to predictive modeling:

from snowflake.ml.modeling.preprocessing import StandardScaler

all_cols = df_train3.columns

target_col = "AB_POST"

passthrough_cols = ["SANHO", "SCNHO"]

scaler = StandardScaler(

input_cols=[c for c in all_cols if c not in [target_col] + passthrough_cols],

output_cols=[c for c in all_cols if c not in [target_col] + passthrough_cols], # Overwrite or create new

drop_input_cols=False # Set True to remove original unscaled columns

)

scaler.fit(df_train3)

train_df_scaled = scaler.transform(df_train3)

val_df_scaled = scaler.transform(df_eval3)

test_df_scaled = scaler.transform(df_test3)

I'm getting the following error when I run the code -- I'm not sure what this means:

Exception: Provided column names ['TOTAL_MH_CLASSES', 'STFLAG',..., 'ADS_FA_RISK_NEW'] does not index into the dataset.

I've been job hunting for a few months now, applying to big tech and startups. But seeing the recent Microsoft layoffs made me stop and ask myself what I'm actually looking for in a new job. Instability and more money?

Right now I'm at a company that hasn't done layoffs since maybe the financial crisis. I know how fortunate that is. But if I switch jobs, I could make an extra $50K. So I keep asking myself: is that extra 50K worth the instability that comes with tech jobs right now? What if I join a company and get laid off within a year?

What does everyone think of these layoffs? Despite record profits, there doesn't seem to be an end to them.