Jess Ramos Data

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10/15/2026

SAVE THIS!! THIS is the hottest new data role emerging in 2027, and I bet you haven’t heard of it yet!

Companies are really starting to invest a lot of money into Token Analytics, including salaries from $120K - $190K.

I learned about it from interviewing Salesforce’s Chief Data Officer as we discussed the declining trend of tokenmaxxing.

Companies are now instead focusing on whether or not AI is producing an actual business ROI.

This new role is all about Token Analytics, aka AI Tokenomics and AI FinOps, and it sits at the intersection of AI usage, cost, and business value.

These roles are evaluating the cost of AI investments, tracking token consumption and efficiency across teams, and forecasting costs across models and providers.

And they’re answering questions like:
↳ Did AI save time after accounting for human review and corrections?
↳ Did the quality of the output improve?
↳ Could a less expensive model deliver equally good results?
↳ What does each successfully completed task actually cost?
↳ Is the business benefit greater than the full cost of implementation?

If I were building a data portfolio for 2027, I’d build projects around token analytics because it’s truly going to be the hottest place to be. You’ll still need alllllll the regular data skills like SQL, data visualization, business acumen, and storytelling, but you’ll be an expert in AI and tokens.

And if you’re a company, I highly suggest hiring people to help with this sort of analysis. Otherwise, your cost will be completely out the window with no guaranteed ROI.

10/14/2026

Tokenmaxxing is SOOOO 2025

And now everyone is moving on from it

Last year a lot of companies used token usage as a proxy for how much their organization was adopting AI.

The thinking was that more AI usage meant people were being more efficient and better.

But in 2026 it’s turning out to be really expensive.

We’ve also realized that burning more tokens doesn’t mean you’re getting more value.

After interviewing a lot of execs this year, the conversations have moved to two things: data and security

↳ Data:
The majority of enterprises don’t have data that’s actually ready for AI. When agents are making decisions on their own, they’re often missing context or working with data that’s missing, incomplete, or flat out inaccurate. That output is passed to business users that just take the confident answer and run with it on big business decisions.

↳ Security:
AI agents have their own identities just like human employees. Gartner predicts the average Fortune 500 company will have 150,000 AI agents running by 2028, and some of them are already accessing data they shouldn’t and handing it to people who shouldn’t see it either.

Securing agents looks a lot like securing human identities just with a lot more complexity.

Let’s leave tokenmaxxing in 2025 and focus on data and security in 2026⚡️

10/13/2026

Data and Cybersecurity expert Jess Ramos | Tech, AI, & Data⚡️ and Jas Wong | AI & Cyber Security talk about their AI fail moments 😅

↳ API keys pasted straight into a chat (BIG no no)
↳ Wispr Flow picked up Desperate Housewives dialogue mid-dictation and mixed it into the prompt which created a crazy output lol
↳ A recipe that lost HALF its ingredients before the cake even went in the oven
↳ AI-maxxed so hard for LinkedIn posts and newsletters that they lost their own voice for MONTHS

This just shows the data and context you give AI shapes everything it gives back.

And sometimes the biggest fail isn’t a glitch, it’s forgetting what only YOU can bring to the table.

What’s your AI fail?

10/12/2026

Yeehaw 🤠 defending all southerners

10/12/2026

You can get into almost ANY conference for FREE.

…if you’re willing to ask.

I asked about how we both get into events without paying, and it was simpler than most people expect. (and most times we get PAID to go for free!)

↳ You can offer to recap the event and interview executives and hand the footage to the conference, even if you never post it on your own channel, and you’d be surprised how many will give you a badge for that

↳ You can also reach out for a press pass/media badge. Most conferences have a press page, so I email them and ask for a free pass, or I reach out to people on and see if there’s a way in through a brand partnership

↳ Speaking is the one people underestimate the most, because conferences will give speakers a free pass and usually even airfare and a hotel room if you’re willing to get on stage.

And guess what? The more events you go to, the more you get seen and the more people want you at THEIR event.

Once you make that first investment and effort to show up and make content, people start reaching out to you.

Now I have brands coming to me because they want me at their events, since they’ve seen me at all the other ones… and now I can charge BIG for my presence and content.

Most conferences put so much work into their programming that they aren’t thinking about content & online presence, which makes it an easy way in if you’re able to help with that.

Just asking goes a long way ✨

10/07/2026

Should you be a data scientist or a data analyst?

Sundas Khalid and I broke down Data Analyst vs. Data Scientist in under 30 seconds

↳ Salary
Data analyst: $80K to $160K
vs.
Data scientist: $130K to $150k

↳ Top skills
Data analyst: Analysts LIVE in SQL, data visualization, business acumen, plus communication and storytelling
vs.
Data scientist: Needs SQL too, but add Python, statistics, and machine learning on top of communication and storytelling

↳ Education
Data analyst: No degree needed as long as you have the right skills and build lots of projects and a portfolio. If you do want one, Math, Computer Science, MIS, or business are all great options.
vs.
Data scientist: A bachelor’s or master’s in statistics, machine learning, or computer science is needed or at least highly recommended.

The right role for you depends on what kind of work actually excites you and where your current skills will lend you a head start.

10/07/2026

In 11 months I negotiated from $71K to $153K as a data analyst in tech 👩🏻‍💻

Because whoever speaks first in a salary negotiation LOSES!!

They ask “what are your salary expectations?” and there’s no winning in answering that for them.

If you go too low you risk leaving money on the table, and if you go too high you risk getting overlooked for a different candidate.

Here’s what I learned:

↳ Always say you’re interviewing elsewhere, even if you’re not, that way you come across as in demand.

↳ If you’re getting close with a company mention you’re in final rounds somewhere else too because that’s INSTANT leverage.

↳ Take your graduation year off your resume so to not hand them a reason for age bias.

↳ Advocate for yourself internally, even if it doesn’t work the first time. Collect data and keep track of your records i.e. your wins and your accomplishments.

↳ Job hop every 1-2 years early in your career. You’ll never have more leverage than when you’re negotiating your way into a new job.

Stay toxic and negotiate your worth💅

10/07/2026

Networking at conferences made easy!!! Did you know LinkedIn has a QR code you can scan for your profile??

10/05/2026

comment “AI” for my full synthetic data tutorial Youtube video! save for later & follow for more!

Save for later & follow for more!

STOP sleeping on synthetic datasets!

Kaggle is fine when you’re learning but if you want to actually show employers you can build like a real data engineer, you need to create your own datasets from scratch.

You’re designing schemas, setting constraints, adding realistic messiness. That’s the real skill.

I tested this with ChatGPT and it crushed Claude and Gemini on realism and ease of use.

Fill in this prompt and you’ll have production-ready synthetic data in MINUTES.

You are a data engineer generating a realistic synthetic dataset for [INDUSTRY] and [PROJECT TYPE OR PURPOSE].Can you generate [NUMBER] realistic datasets with the following requirements.Create an [TABLE NAME] table with [ROW COUNT] rows and columns: [LIST REQUIRED COLUMNS], plus any additional realistic columns you think would be useful. [PRIMARY KEY] is the primary key. [FOREIGN KEY 1] and [FOREIGN KEY 2] are foreign keys that connect to the [RELATED TABLE NAME] table. Ensure that [NUMBER] foreign key values exist in the related table but do not appear in this table (to simulate missing relationships).Create a [DIMENSION TABLE NAME] table with [ROW COUNT] rows and columns: [LIST REQUIRED COLUMNS], plus any additional realistic columns. [PRIMARY KEY] is the primary key and connects to the first table. Ensure that [NUMBER] records in this table have no matching rows in the first table.For both tables, include high variation across values, non-even category distributions, and realistic data patterns. All ID fields should be random numeric values only (no letters).[Add in any other requirements, constraints, or behavior rules]Return each table as a separate, downloadable CSV file.

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