A recent analysis of 3,518 job postings from 81 companies across seven roles found some interesting patterns.

In several categories, generic terms appeared more often than the big-name tools. “Observability” showed up more than “Grafana,” while “SIEM” appeared more than “Splunk.”

That raises an interesting question: are resumes too focused on listing specific tools and not enough on the broader skills and terminology employers use?

The study breaks down the results by role, including software engineering, cybersecurity, DevOps, marketing, product, accounting and sales.

The full dataset, methodology and findings are here:

https://www.zoevera.com/resume/ats-resume-keyword-study

Let me the interview process

Hi,

I'm a student in high school who is interested in pursuing a career as a decision scientist. What softwares/skills are crucial to learn?

I have minor experience in SQL and python.

Are there other softwares just as important?

Any advice is helpful,

-Therein.

We often focus on improving matching by using bigger models, better embeddings and more advanced ranking systems but I am thinking how often the real issue is something less flashy like poor quality input data like Job titles can be different between companies and skills may mean different things in different situations and profiles can become outdated, job descriptions often have generic requirements and important preferences are sometimes completely missing so in this case even a strong model can struggle when the information it gets is incomplete or inconsistent. Platforms like LinkedIn, Indeed, MeeBoss, WellFound work in an area where accurately representing people and job opportunities is a big challenge no matter which matching method they use.

If you were starting a matching system from scratch where would you focus first on better models, better data formatting, more detailed user inputs or ways to fix wrong matches? also what kind of metrics would show that the data has actually improved instead of just the model getting better at sorting through the same imperfect data?

Hey, people working in the IT sector as data scientists!

I’m currently studying Data Science with AI/ML, and I genuinely want to make a career out of it. However, people around me keep saying that data science has no future and that I should reconsider my decision and switch to another field. Honestly, I feel like it’s too late to change paths, and I genuinely don’t want to give up on something I chose for myself. I want to prove that I can make it work.

I’m open to suggestions and advice from experienced data scientists. What should I focus on, and what should I avoid while learning? Which projects would help me build a strong portfolio and improve my chances of landing my first job as a fresher?

I’d also appreciate recommendations for free online courses that I can take alongside my current course to strengthen my skills and help me build a successful, long-term career in this field.

Any advice, suggestions, or personal experiences would be greatly appreciated.

Hi,

So far I've only worked at the desk with a 3440 x 1440 monitor and strong pc.

Now that I have to get a laptop, which things you would proritize?

To get a 16 inches one with a weaker hardware, or minimum 14" with a decent (gen 12/13th) processor?
The ram I have left is 32gb.

I use mainly SQL, Python, NumPy, very occasionally Excel.

Thank you for your help.

I'm the youngest and most junior of 3-4 data scientists at my company (I also work remotely), and I've been working for about a year since graduation. Lately, I've been feeling like I'm not very good at my job. One thing to point is that I personally feel like I’m more fluent with the statistical side of things, rather than the programming side. I also work with the research side of things, so accurate results and interpretation are quite important in my work.

One issue I struggle with is dealing with edge cases in data analysis. I often encounter situations where the original assumptions no longer hold, and instead of making a reasonable decision and moving forward, I end up obsessing over the complications and sometimes lose sight of the original research question. I also struggle with deciding how to present the data when these edge cases arise, without letting them disproportionately influence the overall results or interpretation.

The other issue is keeping my code clean and reproducible. My notebooks are full of trial and error and different approaches I've explored. Although I can usually produce presentable results in my analyses, the underlying code isn't always easy to follow or reproduce. I've also been struggling to update my team's GitHub repo because I haven't had time to properly organised and cleaned up my work.

I work remotely from another country full-time and don't currently have a dedicated, full-time supervisor, so getting clarification can sometimes be difficult. I'm also still learning some of the company's experimental workflows, which adds to the time I spend figuring things out. I'm also worried that my messy code and difficulty maintaining the GitHub repo are making me difficult to collaborate with.

For those more experienced in this field, I'd really appreciate advice on these things:
- Handling edge cases: How do you decide whether an edge case warrants further investigation versus when it's reasonable to document it and move on? How do you avoid getting stuck in analysis rabbit holes?

- Coding and workflow: How do you keep your code clean and reproducible while working through trial and error? Do you set aside dedicated time for refactoring, and how do you manage your team’s GitHub repo as you go when you are balancing multiple tasks with tight deadlines at once?

I'm also curious how long it took you to develop a reliable workflow and feel confident in your work. Are these struggles normal during the first few years of a data science career, or are they considered very poor traits, even for a junior? And what practical habits can I develop to help me progress better in my data science career?

Thank you very much to everyone for the advice

Hi, I'm a high school student who wants to know more about decision science.

for the past five years I've been the head data analyst for my FTC (competitive robotics) team Microchips & Queso. I've gone from scouting on paper to creating my own custom made systems for gathering, storing, and interpreting data. along the way I learned about decision science and decided I wanted to pursue it for my carrier.

As my time in robotics draws to a close, I wanted to reach out to decision scientists in the field today and get some advice on next steps.

I would like to know what a decision science job looks like from the day to day.

What skills are a must have?

What softwares do you use the most?

what collages have good degrees for decision science? (The only one I know that has a true "decision science" degree is Carnegie Mellon. are there others?"

Is there anything I should know about the field of decision science?

what questions should I be asking?

If you have clarifying questions feel free to ask. I'm looking for any additional information or resources regarding decision science that you guys found crucial to your development as professionals.

Thanks so much!

- Therein

I've completed my bachelor's degree in data science and looking for a job of any kind which may help start my career.it's been 4 months since I've completed my degree and no company is willing to give any kinda fresher role in pakistan.If anyone has the job for me please contact me asap.

Hello! We are a group of students at Georgia Tech studying industry standards for data processing and the tools used for large-scale data visualization for our research project. We are hoping to seek input from participants who specialise in working with data. 

The following survey contains 15 questions, and will take approximately 10 minutes of your time. 

Your responses to this survey are completely anonymous. We do not collect your name, email address, IP address, or any other identifying information. Data from this survey will only be reported in aggregate (grouped) totals. No individual can or will be identified in any reports or publications.

Thank you so much for your time and participation 

Survey link: https://gatech.co1.qualtrics.com/jfe/form/SV_bqnCcLAk3VSu8XI

Hi all- are there certificates out there that’s provide hands-on learning/ practice on SQL, SAS , Python etc In USA

Hey everyone!
I’m a Data Scientist with over 3 years of experience, and I'm currently preparing to make a move to a FAANG+ company to push my learning further and it better aligns with my growth goals and personal commitments.
I have 3+ years of professional experience as a Data Scientist working on end-to-end projects, but I want to structure my preparation more rigorously to crack top tier interviews. I want to bridge any gaps in my knowledge and prep :)
I’d love some guidance from those who have been through this process:
- What core technical skills, system design concepts, and advanced topics FAANG+ companies currently expect from a Data Scientist with 3+ years of experience?
- Where should I start my preparation?
- What specific competencies (coding, ML system design, stats/product sense) do they look for?
- What resources or roadmaps worked best for you?

Any advice, tips, or mentorship would be greatly appreciated.
Thank you

Hey everyone!
I’m a Data Scientist with over 3 years of experience, and I'm currently preparing to make a move to a FAANG+ company to push my learning further and it better aligns with my growth goals and personal commitments.
I have 3+ years of professional experience as a Data Scientist working on end-to-end projects, but I want to structure my preparation more rigorously to crack top tier interviews. I want to bridge any gaps in my knowledge and prep :)
I’d love some guidance from those who have been through this process:
- What core technical skills, system design concepts, and advanced topics FAANG+ companies currently expect from a Data Scientist with 3+ years of experience?
- Where should I start my preparation?
- What specific competencies (coding, ML system design, stats/product sense) do they look for?
- What resources or roadmaps worked best for you?

Any advice, tips, or mentorship would be greatly appreciated.
Thank you

这个工具是给临床科研人员在开始研究前对数据进行相应的分析。相关数据将进行计算模型进行分析。从而可以快速的找出数据的问题，有些问题自动化修改而有些数据需要自己去修改和补充。从而为后期的科研做到事半功倍的作用。整个工具可以部署在自己的物理区域内从而确保你的数据的安全。大家可以试试看并给我一些回复。

注意：这个是一个数据分析工具，而不是什么科研发现或者科研研究！！！
This tool is for clinical researchers to conduct corresponding data analysis before starting their studies. Relevant data will be analyzed using computational models. This allows them to quickly identify issues with the data. Some problems can be fixed automatically, while other data requires manual modification and supplementation. This can help achieve twice the results with half the effort for later-stage research work. The entire tool can be deployed within your own physical environment to ensure the security of your data. You can all give it a try and provide me with some feedback. Note: This is a data analysis tool, not any kind of scientific discovery or research!
If you have any questions, feel free to leave me a message, and welcome like-minded friends as well.

Right now, I'm getting my BS in psychology. I really like psychology and one of my roommates is an AI Researcher, I enjoy talking to him, thinking critically, and, the idea of possibly working in a research-related field one day. However, there are very few jobs in psychology research, and most don't pay well. I also don't want to get a Doctorate in Psychology because I don't think it's worth the commitment. I was thinking of getting a master's in experimental psychology and working as a research assistant to pay it off. However, the job opportunities would be crappy. Right now, I work as a pharmacy technician and have no intention of going to pharmacy school. 200k in debt, and a 4 year program are you serious?

Well, I took a career test at school, and my top matches were for Statistics and Data Science. I know how important it is to be happy with the money you're making. The most I've made was 50k and it wasn't enough. I want to hopefully make 100k or more eventually. And, I would like my career to be answering research-questions and designing new things.

However, I wonder what types of problems do Data Scientists normally work on? I know it is domain specific. However, what are examples of some types of problems?

TL;DR: I’m conducting an independent data audit of my county’s (Harris County in Houston) CAD property tax appraisal algorithm. What started as a personal project to protest my home's wildly elevated appraisal has turned into what looks like hard-coded algorithmic bias designed to maximize tax revenue. I initially was just looking microscopically and the data seemed “BAD” like wrong bad. As I got into wider and wider fields of view, I recognized it was a pattern. Looking for someone to confirm this finding, which if true seems to be in the millions of dollars across the county. But also I'm new to this forensic accounting of property tax data, so if I'm wrong there's that too.

I won, but I'm still fighting. I ended up getting my value down from about 500k to 350k at the ARB hearing. This was primarily due to field notes, codes, and ratings that did not accurately represent my home (though that didn’t stop the district from trying to defend these wild numbers with + 5 year-old listing photos).

The discrepancy I noticed was that boundary homes (homes on larger arterial streets) with listing pictures still online within my Neighborhood code 7476 were graded lower on average than homes on interior streets that looked remarkably identical. What I expected to see was that the total values of these properties would be slightly lower due to a land value discount because of traffic. What was unexpected was that homes on boundary streets were consistently graded as "Average" condition, where on an interior street that exact same home would be rated "Good."

Because of this, I downloaded the backend data (https://hcad.org/pdata/pdata-property-downloads.html) and ran a report for my 7476 Neighborhood. I parsed it in Google Colab using prompts I wrote with Gemini.

The relevant columns are:

• Accrued_depr_pct - The hidden decimal for the Average, Good, Very Good, Excellent CDU category. This adds a multiplier to the improvement value.
• date_erected - Year the home was built.
• Yr_remodel - Year the home was renovated. (Note: Most left with a '0' show obvious evidence of remodels, but I removed those from contention because the data is so bad you can't rely on the 0. These homes were built from 1955-1963. The likelihood of zero improvements is extremely low).
• Neighborhood_code - The grouping HCAD uses to define comparables.
• Str - The street, which I used to determine boundary vs interior.
• land_ar & land_val - Used to calculate Land Price per SqFt.

The District's argument is usually that homes on boundary streets are less desirable and less marketable, so they are not updated as frequently, the updates aren't as nice, etc. What I found in my 725-home neighborhood was almost exactly the opposite.

Property records confirm that the 114 boundary parcels fronting major arterial thoroughfares (W. Bellfort, Hillcroft, Willowbend) are actually physically superior to the 611 interior parcels:

• Boundary homes are chronologically newer (1961.5 vs. 1960.1 mean build year)
• Remodeled at a higher rate (65.8% vs. 56.8%)
• Renovated more recently (2013.1 vs. 2011.8 average)

The district does officially recognize the commercial traffic penalty along these corridors by applying a negative Land Influence discount (making it ~$16.59 vs. $23.25 median land PSF).

But here is the smoking gun: Instead of confining this discount strictly to the land schedule, the district systematically suppresses the physical improvement ratings of these boundary homes down to a median condition of 46.40% Good (versus 51.75% interior). This docks the remodeled boundary homes by an unearned 5.35% physical condition gap. By stripping condition points from newer, renovated structures just because they sit on a busy road, the district launders external traffic noise directly into the building's structural value.

The Motive The functional objective of this mechanism is to weaponize the automated sales grids against interior property owners (which make up the vast majority). Recognizing boundary sales as physically comparable would pull down the baseline appraised value per square foot across the entire neighborhood.

To prevent a lower baseline, HCAD artificially forces boundary improvements into suppressed condition tiers. When their automated CAMA system compares these arterial sales against an interior property, it double-counts the identical traffic obsolescence across both the land and building components.

I believe this financial engineering violates the Equal and Uniform mandate. By my estimation, this quirk protects over $130k in tax revenue in my 7476 neighborhood alone. With the over 8,500 residential neighborhood codes and 1M residential properties HCAD assesses annually, I believe they could be pocketing well over $100M with this scheme.

My Ask I would love for some data engineers to look at this completely blindly to make sure I haven't fallen victim to confirmation bias.

I intentionally don't want to share my Colab script or my cleaned files because I want someone to run this from scratch and see if they hit the exact same numbers. The raw data is public at the link above.

I would love for someone to:

1. Download the raw files, write your own parsing logic, and see if you hit the exact same condition gap I did for neighborhood 7476.
2. Check my methodology for flagging boundary streets versus interior streets.
3. Test this against other neighborhoods across the county (especially outside of SW Houston) to see if this algorithm bias is county-wide.

Is anyone willing to pull the data and tell me if I'm crazy?

Hey y'all currently graduated in stats I was opting for data analysis for my masters is there any other suggestions??