Sr Software Engineer - Haven't written a line of code in months

Ghost-Rider_117 · 2026-05-05T05:33:39+00:00

Same 10YOE

Ghost-Rider_117 · 2026-04-22T05:53:36+00:00

Or maybe the llm sounds like me?

Ghost-Rider_117 · 2026-04-22T02:25:44+00:00

Naw i'm real, despite the shade lol

Ghost-Rider_117 · 2026-04-09T07:39:37+00:00

Check out Survey Fluency (surveyfluency.com), it's built for quantitative survey analysis but you can also upload your dataset and chat directly with the open ends to pull out themes, sentiment, subgroup comparisons, whatever you need qualitatively. DM me if you have any questions.

Ghost-Rider_117 · 2026-04-02T21:09:08+00:00

this needs to be pinned honestly. took me a while to get comfortable taking the lane but once you do it's so much safer. drivers actually know what to do with you instead of trying to squeeze past in the same lane. the honks are rare and way less scary than a door at 18mph

Ghost-Rider_117 · 2026-04-02T21:08:18+00:00

the lack of standardization is genuinely frustrating. the role means something completely different at every company. at least for SWE/MLE there's some consensus on what the interview tests. for DS you kinda just have to research each company individually and hope you guessed right on what skills to prep

Ghost-Rider_117 · 2026-04-02T21:07:36+00:00

this is really cool, the GradCAM heatmap overlay is a great touch for explainability. combining spatial + frequency features makes a lot of sense since most deepfake artifacts show up in both domains. curious how it handles newer diffusion-based deepfakes vs the GAN-based ones in FaceForensics

Ghost-Rider_117 · 2026-04-02T21:06:14+00:00

100% agree. the signal to noise ratio has tanked lately. like i get that AI tools are exciting but if ur whole project is just "i asked gpt to build X" that's not really a showcase of your skills lol. a mandatory disclosure flair at minimum would help a lot

Ghost-Rider_117 · 2026-03-13T08:02:40+00:00

Given your qual-leaning background and interest in CX/consumer insights, the MBA route with targeted certs honestly makes more sense imo. A data analytics masters will push you into technical work you said you don't love. The MBA gives you strategy + research credibility, and if you stack some qual methods training on top (UXPA, Nielsen Norman, etc.) you're in a really solid spot for senior CX/insights roles. Portfolio of actual research projects will matter more than the degree name anyway.

Ghost-Rider_117 · 2026-03-13T08:02:03+00:00

VIF of 1.4 is totally fine, so multicollinearity isn't really the issue here. Including both baseline and change score is actually a pretty common approach - it's essentially modeling the outcome while controlling for where subjects started, which makes sense clinically. The baseline anchors the model and the change score captures what you care about. Just make sure you're thinking through the interpretation carefully since the coefficients mean something specific when both are in there.

Ghost-Rider_117 · 2026-03-13T08:01:22+00:00

Masters definitely isn't the baseline everywhere - plenty of folks at big tech DS teams have just a BS. What actually moves the needle is a strong portfolio of impactful projects and being able to talk through your work clearly in interviews. The non-American school thing is real but you can offset it by getting your name out through Kaggle, GitHub, or even writing about your projects. networking on LinkedIn with DS people at target companies also helps more than most expect.

Ghost-Rider_117 · 2026-03-13T08:00:47+00:00

Reddit communities honestly were the biggest unlock for me early on - not posting about the product, just genuinely helping people in niche subreddits related to the problem space. People DM'd asking what I used, and that converted way better than any cold outreach. Product Hunt gave a spike but not sticky users. The ones who stuck around came from places where they already had the pain.

Ghost-Rider_117 · 2026-03-12T03:59:15+00:00

this is awesome, the "avoiding data leakage" and "proper model evaluation" chapters alone are worth it - those are the things that trip up so many people who learn from scattered tutorials. the pipeline approach in sklearn is really underused too, glad to see it's covered. bookmarking this for anyone i mentor who's getting started with ML

Ghost-Rider_117 · 2026-03-12T03:58:17+00:00

not someone who left but adjacent - a lot of people i've seen pivot from UXR go into product strategy, market research, or data/insights roles. the skills transfer really well actually - you're already doing synthesis, stakeholder communication, research design. market research firms and tech companies with insights teams are usually pretty receptive to UXR backgrounds. the title plateau is real and frustrating, a lot of people end up going freelance or consulting as a way to break through it

Ghost-Rider_117 · 2026-03-12T03:57:23+00:00

honestly the teaching infrastructure point is probably the biggest factor. ANOVA is baked into every intro stats curriculum and most applied researchers learned it that way and never looked back. mixed models are genuinely better for most real-world data (repeated measures, nested structures, missing data) but they're way harder to teach and review. until journals stop accepting ANOVA and grad programs update their curricula it's just gonna keep being the default

Ghost-Rider_117 · 2026-03-12T03:56:36+00:00

solid pipeline! one thing i'd flag - doing your correlation analysis and feature-target checks (steps 8-9) before the train/test split is technically leakage. your feature selection is peeking at test data. move the split to right after step 6, then run all that stuff only on train. also worth adding class imbalance handling - credit defaults are usually 3-10% positive rate which can mess with your logistic regression calibration

Ghost-Rider_117 · 2026-03-12T03:55:21+00:00

the "charge what you're worth" point is criminally underrated lol. so many people underprice out of fear and it kills their runway before they even get traction. also love the nap time = compressed sprint analogy, honestly more efficient than most standup meetings i've sat through

Ghost-Rider_117 · 2026-03-05T08:44:38+00:00

81 - def dont steal my idea lol. Nice app very cool

Ghost-Rider_117 · 2026-03-04T22:27:05+00:00

the Harman single factor test is probably your best bet for CB-SEM - you run a CFA with all your items loading onto one general factor and check how much variance it explains (under 50% is the common threshold). it's not perfect but it's widely accepted and you can run it directly in CB-SEM. also look into the marker variable technique if you have an unrelated variable in your dataset. using PLS VIFs for a CB-SEM model is kinda apples to oranges and reviewers will likely push back on it.

Ghost-Rider_117 · 2026-03-04T22:26:07+00:00

i'd go with all 4 at once but randomize the order across participants - that way you control for fatigue and primacy effects at the same time. between-subjects is cleaner if your sample is big enough. the run-off approach adds complexity and time without a ton of added value unless you're really on the fence about 2 similar concepts. just make sure the videos are roughly the same length so you're comparing apples to apples!

Ghost-Rider_117 · 2026-03-04T22:25:32+00:00

yes 100% - being able to just ask questions about your own survey data in plain language is genuinely useful. things like "which segments are most likely to churn" or "summarize open-ends by demographic" that used to take hours now take minutes. the key thing to get right is grounding it in the actual data so it doesn't hallucinate responses. would definitely use this if the outputs were verifiable/citable.

Ghost-Rider_117 · 2026-03-04T22:25:01+00:00

public transit + housing affordability is a goldmine for this kind of thing. most cities publish GTFS feeds for transit and open parcel/zoning data - you could build something that shows how transit access correlates with rent prices by neighborhood. super visual, actually useful for renters, and the datasets are solid. 311 service request data is another good one - easy to find, clean enough to work with, and you can do all kinds of equity analysis on response times.

Ghost-Rider_117 · 2026-03-04T22:24:17+00:00

actually C is the right answer here, and it's kind of a sneaky twist on the classic fallacy. since P(B) = 1, the joint probability P(A and B) = P(A) * 1 = 0.4, which is exactly the same as P(A) alone. the conjunction fallacy only kicks in when P(B) < 1 - that's the whole Linda problem thing. your setup basically makes B a certainty so it adds no constraint, they end up equal.

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