Co-Founder @ Stan | building a $BN company 🚀

Hi, I’m Vitalii 👋🏻

Over the past 3 years, I’ve built a $30M ARR business with 30 people.

A $1M/employee company.

Now, we’re building a category-defining brand—with $100M ARR in sight.

To do that, I focus on Culture, People, and Product.

Everything that got me here is self-taught.

Born and raised in Russia, I started working at 13—packing parachutes and skydiving. 🪂
By 18, I had 800+ jumps and enough saved to pay for my first year of college.

Jumping out of planes redefined my relationship with fear.
Once you commit, you act. Without hesitation.

I learned English in one year to study abroad—still the hardest thing I’ve done.
That brought me to Canada, where I earned a degree in Chemical Engineering.

I never used it.

My side projects opened doors for me—first to Deloitte, then to eBay.
But the real work happened outside the job.

I made time to build:
9–5 for the paycheck,
5–9 (both morning and night) for what I actually cared about.

That’s where I proved to myself—nothing is impossible.

In 2021, I met my cofounder. We went all-in and started building Stan.

On my LinkedIn, I write about:

→ Founder war stories (unfiltered)
→ Building a $1M/employee startup
→ Creating top 1% culture

🎧 Follow me here if you're building—or want to build—something great.

People I work with would describe me as:

→ I make things happen
→ Zero tolerance for mediocrity
→ I build and run top 1% talent-density teams

🫂 DM me on LinkedIn if you'd like to work at Stan.

But know—it’s likely to be the hardest thing you’ve ever done.
And worth it. For the right person.

—

On LinkedIn, I share stories for founders building what’s next—
The ones who care about People, Culture, and Redefining what's possible.

If that’s you,
Follow me. Drop a comment. Or just say hi 👋🏻

Vitalii Dodonov

I've just hired Emily Talas to make Stan the #1 place to work in Toronto 🇨🇦

A day later: "You're so lucky to have her."

I got 7 texts like this following the announcement ^^. Must be doing something right 😅

Here's a stat I feel vulnerable to share:

50% of our candidates from traditional channels don't work out by month 3.

At the same time, 
90% of employee referrals stay indefinitely and create disproportionate value.

There's signal in that.

And we're acting on it ✅

Starting now, we’re going 90% community-based hiring.

When I met Emily, I knew almost instantly she'd be the one:

Born to a family of entrepreneurs. Working since 14. Found her ikigai in Community x Tech (how rare is that!?). People I respect texted me about her.

I bet on Emily because the talent I'm after doesn't talk to recruiters: 
"The best people are never unemployed."

But on occasion, they hang out with other cool people. 
Emily's mission is to create the occasion.

In 2026, Emily's likely to drive 90% of our hiring in Toronto.

Think hackathons, hacker houses, private dinners, collaborations, and special events giving unknown talent a chance to meet us.

🇨🇦 Toronto — what's one event you wish existed here? We're painting a blank canvas, share some ideas!

My first bet of 2026: $20,000 on builders in Canada 🇨🇦

Build with AI? On Jan 23 👇

I’m inviting 60 builders to embark on a weekend-long sprint to create something new at the edge of AI.

Details:
🛠 Participants: 60 builders
⏰ Duration: 2 days 
🎯 Theme: AI × Voice × Video
👥 Team: Solo, with friends, or AI teammates

Each participant/team gets a $200 tech credit and competes for:

$20,000 cash
30-min 1:1 with me (ask anything you want)

To help spread the word, please repost this and tag anyone who should see it!

This is an invite-only event.

📩 To apply, leave a comment describing the most impressive thing you’ve built in one line.

CRA paid me $750K for building in Canada 🇨🇦

And now I'm giving it back - starting with $20K on Jan 23. 

In 2021, we established our office in Canada.

3 years later: $30M ARR. 30 people. One of the fastest-growing startups in the country.

My vision was to build a $1B company with 10 engineers - I'm evolving that now.

Since then, our Toronto office expanded far beyond engineering and has become HQ for 20+ brilliant people building at the edge of technology and entrepreneurship.

There are 2 major tech hubs in the world — SF and Toronto.

And while SF has access to capital and a developed VC ecosystem, California seems to be working against its own interest.

I mean… who wants to become a billionaire in California now? 👎

Anyway — Canada paid me $750K last year for building here.

And I'm paying it back to Canadian builders.

January 23. Hackathon in Toronto. 60 builders. $20,000 grand prize.

Come join us — and let the prize be the seed funding for your company (or use the opportunity to interview at Stan — whatever your ambition is).

Invite-only (curating a high calibre room!)

📩 To apply, drop your proudest project in one sentence in the comments. I'll reach out with an invite if it resonates.

Traditional recruiting is dead. That's why I'm paying $20,000 to a stranger in January.

I've hired 40+ people.

Together, we've built a $30M ARR startup. Profitably.

One thing in common among my best hires:

I wouldn't call them if all I saw was their resume. Take a look:

Esther, now my EA, has a fashion management degree. She got the job by walking into a machine shop and producing a mechanical drawing — before we even met 💅🛠️

Jason (JSON) won a hackathon last year he couldn't legally join because he was 17. I hired him on the spot (with his parents' approval) and he's built a $1.4M product with me in 9 months 🧑🏻‍💻

Ricky, now Head of Engineering, came from a company I'd never heard of. Did fine in the interview (on his birthday! 🎂). Then called me at 10 PM with a better solution he couldn't stop thinking about. 

Resumes can only tell you so much — unreliable signal.

So instead of reading resumes, I host hackathons. Give builders a reason to come. 

Then watch.

So here we are 👇

January 23. Toronto. 2-day hackathon on the edge of AI.

Win $20,000 — and (or!) a job building a $1B company. Invite-only. 

📩 To apply: tell me a story of what you've built that you're most proud of in the comments.

I'm paying $20,000 for 2 days of someone's work on Jan 23.

Early in my career, 

I was making $8,000/mo writing code for a big company, while hacking away on side projects. I always wanted to work for myself.

One day, I was in a room with an entrepreneur who was upset about fees he was paying for software. So I asked:

V: How much are you paying?
M: 10%.

V: And how much money is that?
M: $50K/year.

My mind goes: no wonder he's upset. I bet I could do a simple Stripe integration with 3.4% fees. I already did something similar for my side project.

V: If I could get your fees down to 5%, how much would that be worth to you?
M: I'd pay $10K without even thinking.

V: I'm gonna do it for you for $10K.

It took me a weekend — 3 days (nights included), mostly repackaging code I already had across other projects.

I gave him a demo on Monday, and by Friday, $10,000 landed in my PayPal.

That weekend I learned: the right room + the right skills = life-changing opportunity. 

And now, I want to put you in that room 👇

January 23. Hackathon in Toronto. 60 builders. $20,000 grand prize. 
Invite-only. 

📩 To apply, drop your most impactful build story in the comments — I'll personally reach out to the builders I want in the room.

I hired my Executive Assistant because of her napkin drawing 📜

Her name is Esther.

A year ago, I was looking for an Executive Assistant. 

Looking for the top 1%, I needed a way to test problem-solving and initiative.

So I gave candidates a creative test:

I was building a frame for a flying car as a passion project at the time.

How many ppl know anything about flying cars anyway? 🤷‍♂️

I wrote down an abstract description of a few parts I needed.

The assignment was to research machine shops and find a way to manufacture them 

When I reviewed Esther's submission, she had a picture of this drawing inside (see the image).

I invited her to my office:

V: "Do you know anything about manufacturing?"
E: "No… I study business and fashion"
V: "How did you get this image?"
E: "They drew it for me at the machine shop"
V: "You went to the machine shop?"

(I reviewed a few dozen submissions by then. All candidates just called).

E: "I called first but what they were saying didn't quite fit what you were looking for"

E: "I figured it'd be easier if I just go there to figure it out. "
E: "So I just walked over." 

V: "That must've taken hours. Why invest that time?"

E: "Umm... I didn't really think about it. I wasn't getting what I needed. So I did what I had to do."

Having heard this I knew the interview was over.

Anyone who thinks like that will succeed in anything they do.

She got the job.

And since then, Esther was behind a number of very unique experiences we created at Stan:

Like making the big $750K CRA cheques, organizing Hacker Houses in SF and hosting AI Hackathons on Toronto. 

Congratulations on your 1 year at Stan, Esther!

undefined undefined

Executive Assistant @ Stan

Stan

HackAI 2.0 starting tonight. It’s time to build 😤

New Show: Emily in 𝙿̶𝚊̶𝚛̶𝚒̶𝚜̶ Stan (or "How to get a Founder to hire you")

A story in screenshots 👇

Hiring - Emily in Stan

I'm paying $20,000 in January to the winner of my hackathon. But the real prize isn't the $20K.

The room matters more than the prize.

When I was 13, my dad gave me a gift — working alongside him.

He's a professional skydiver. Extreme sport, expensive hobby. His clients are top-tier business people (natural selection — the sport isn't cheap) 🪂

So in between sessions when we went out for saunas and dinners, I got to sit in and listen to them. War stories, betrayals, deals, schemes, mental models, what made them successful.

I absorbed it all, without even realizing it.

Looking back, I realize — those were the building blocks that led me to a $30M ARR startup, running at $1M/employee before AI.

And that's the opportunity I'm giving you.

Join Stan on January 23 to build for 2 days alongside my team and 59 other builders competing for a grand prize of $20,000.

→ Meet me, my team, and Toronto's best builders.
→ Maybe your future co-founder — or I offer you a job. (It happened last year.)

The $20K is nice. The room might change your life.

January 23. Toronto. Invite-only.

Grab your spot with the link in comments!

I asked a seasoned exec about his worst business decision.

He didn't miss a beat: "Cancelling the 9am."

For 10 years, he ran it daily.
The 2 years he stopped were his worst.

Without that anchor, people drifted in at 9:30.
Add coffee and "how was your weekend" — real work started at 11.

Half the day, gone.

The meetings weren't about updates.
They set the rhythm.

Remove that, and people rebuild discipline from scratch.
Exhausting.

At Stan, when we skip our morning sync, everything drags.

We don't miss the information.
We miss the rhythm.

Humans need structure.
It gives them permission to just start.

What’s your version of the 9am?

Someone asked me what I hope never changes about Stan in 5 years.

Me in 5 seconds: 

"Our commitment to talent density."

I'm building a company where having worked here is the ultimate badge of honor on a resume.

Where any other employer would be fighting to get our alumni on board. 
(If they ever want to work for another company, that is.)

I fundamentally believe the reason we win is this: We attract top 1% talent.

And we put them in an environment where their ability can be utilized to its absolute maximum potential.

With ownership, autonomy, and a set of large, cutting-edge problems they can tackle.

Everything else can change. 
The market, the product, our size.

But for me, it will always be about the talent.

Founders Q: What's the one thing you hope never changes about your company?

One of my favourite Years in Review on LinkedIn. Keep going Alexia - it's okay not to know what's coming. The path unfolds as you start walking.

Putting Humans Back at the Heart of Business | Certified L&D Professional | Soft Skills Expert | Heedership® & Bring Your Asha! Founder | ICF Coach

"It is never too late to be what you might have been." 💜
George Eliot wrote these words centuries ago. I understood them at 43.

𝐌𝐞𝐫𝐜𝐢 𝟐𝟎𝟐𝟓.
Merci pour les rencontres qui sont devenues des amitiés.
Merci pour les doutes qui sont devenus des certitudes.
Merci pour chaque "oui" qui m'a rapprochée de qui je suis vraiment.

This year, I finally found where I belong. Coaching. Delivering Trainings. Learning & Development. Toastmasters: Human Connection.

And as I look at this visual, thank you for the idea Stanley (Your Content Coach) and Vitalii Dodonov, I see my entire "purple" year captured in one image. Learn. Grow. Repeat. The infinity loop between Heedership and Leadership. The three pillars. The magic.

𝐀𝐧𝐝 𝐧𝐨𝐰? 𝟐𝟎𝟐𝟔.
I don't know exactly what's coming. And that's the most exciting part.

What I do know: I'm ready. Curious. Open.

It is never too late to become who you might have been.
And it's never too early to dream about who you're still becoming.

Pure magic. 💜

𝐐𝐮𝐢𝐜𝐤 𝐜𝐡𝐞𝐜𝐤-𝐢𝐧: What are you curious about for 2026?

Data Scientist | 150K LinkedIn | BCBS Of South Carolina | SQL | Python | AWS | ML | Featured on Times Square, Favikon, Fox, NBC | MS in Data Science at UConn | Proven record in driving insights and predictive analytics |

Lead Data Analyst/Data Scientist at Blue Cross Blue Shield of South Carolina, driving revenue growth and operational efficiency through predictive analytics, experimentation, and machine learning.

I build end-to-end data solutions that influence executive decision-making, from churn prediction models and A/B testing frameworks to executive dashboards tracking multimillion-dollar portfolios. My work spans the full ML lifecycle: data pipelines, feature engineering, model deployment, and stakeholder communication.

What Sets Me Apart:

I don't just analyze dat, I translate it into business strategy. Whether it's identifying high-risk customer segments, optimizing conversion funnels through rigorous experimentation, or presenting insights to C-suite leadership, I bridge the gap between technical depth and business impact.

Beyond the 9-to-5:

I'm on a mission to democratize data science education. With 220K+ professionals in my LinkedIn community and 20K+ newsletter subscribers, I share battle-tested insights on SQL, Python, machine learning, and breaking into data science. My content focuses on real-world applications that actually matter in industry, not academic theory.

I've also been recognized nationally:
- Featured in Times Square, Fox, NBC, and major media outlets for career journey
- 3rd Place, Travelers NESS Statathon
- 1st Place, Well4Tech Health Hackathon  
- Favikon recognition for data community contributions

My Technical Stack:

Python (Pandas, Scikit-Learn, XGBoost), SQL, PySpark, R | AWS (S3, Glue, Athena, SageMaker), Azure, Snowflake, Databricks | Tableau, Power BI, Looker | A/B Testing, Bayesian Statistics, Predictive Modeling

Open to discussing data strategy, collaboration, or your journey into data science.

Let's connect: saikumarbysani13@gmail.com

Venkata Naga Sai Kumar Bysani

If you want to stand out as a Data Analyst in 2026, you must build these 5 projects!

(Your portfolio is your resume. Make it count.)

Most aspiring analysts have the same Titanic and Iris datasets on their GitHub. Hiring managers scroll past them in seconds.

These 5 projects show you can handle real-world data pipelines, not just tutorials:

𝟏. 𝐒𝐮𝐩𝐞𝐫𝐬𝐭𝐨𝐫𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬
↳ AWS S3, Glue, Athena, SQL, QuickSight
↳ Shows you can work with cloud infrastructure
↳ https://lnkd.in/di8mbpg3

𝟐. 𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐄𝐓𝐋 𝐏𝐫𝐨𝐣𝐞𝐜𝐭
↳ SQL and Python
↳ Proves you understand the full data lifecycle
↳ https://lnkd.in/d6q5zTwj

𝟑. 𝐍𝐞𝐭𝐟𝐥𝐢𝐱 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐄𝐋𝐓 𝐏𝐫𝐨𝐣𝐞𝐜𝐭
↳ Python and SQL
↳ Data cleaning is 80% of the job. Show you can do it.
↳ https://lnkd.in/dcvhry2q

𝟒. 𝐒𝐐𝐋 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨 𝐏𝐫𝐨𝐣𝐞𝐜𝐭
↳ Pure SQL
↳ SQL is non-negotiable. This proves mastery.
↳ https://lnkd.in/dZwSiQRy

𝟓. 𝐘𝐞𝐥𝐩 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐑𝐞𝐯𝐢𝐞𝐰𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬
↳ S3, Python, Snowflake, SQL
↳ Combines cloud + modern data warehouse skills
↳ https://lnkd.in/dguyNe_Y

𝐖𝐡𝐲 𝐭𝐡𝐞𝐬𝐞 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐰𝐨𝐫𝐤:
→ They use tools companies actually hire for
→ They show end-to-end thinking, not just analysis
→ They prove you can work with messy, real data

Start with these to build your foundation. Then move on to creating your own projects based on problems you actually care about.

That's when your portfolio goes from "good" to "memorable."

Which project are you starting with? 👇

♻️ Repost to help someone land their first data role

𝐏.𝐒. I share insights on data analytics & career growth in my free newsletter. Join 21,000+ here → https://lnkd.in/dUfe4Ac6

Data Scientist | 3+ years in Predictive Analytics, Experimentation & Business Impact | 200K+ Data Community | Featured on Times Square, Fox, NBC

I just built a free interview prep guide for data analysts and data scientists.

And I'm giving it away.

Most people preparing for data interviews are:
↳ Scrambling the night before with no real plan
↳ Googling random questions with no structure
↳ Watching 10-hour YouTube playlists, they never finish

I used Notion Agent to build something better.

𝐖𝐡𝐚𝐭'𝐬 𝐢𝐧𝐬𝐢𝐝𝐞:

↳ SQL questions (from basics to window functions)
↳ Python and Pandas essentials
↳ Statistics and probability concepts
↳ Behavioral interview frameworks
↳ Real company questions with solutions

I used Notion Agent to turn my interview knowledge into a structured guide.

→ Created the entire database structure in minutes
→ Organized questions by difficulty, topic, and company
→ Built custom views for different study approaches
→ Added resources and explanations across 100+ entries

What would have taken me days took one afternoon.

2026 is about building resources that actually help people land data roles.

Comment "𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰" and I'll send you the guide.

Let Notion Agent do your work for you at https://ntn.so/SaiBysani

♻️ Repost to help someone preparing for data interviews right now.

This post is sponsored by Notion.

#NotionPartner #NotionNewYear

January is the best time to job hunt. Most people will still blow it.

Companies are unfreezing budgets. New headcount is opening up. Recruiters are back from PTO.

But here's the problem: your resume still isn't ready.

You'll spend hours perfecting it. Submit to 50+ jobs. Hear nothing back.

Not because you're unqualified, but because an algorithm filtered you out before a human ever saw it.

So I built "𝐑𝐞𝐬𝐮𝐦𝐞 𝐌𝐚𝐭𝐜𝐡" to fix this: 
An AI tool that compares your resume against job descriptions and shows you exactly what's missing.

→ Missing skills ranked by priority (High/Medium/Low)
→ Specific guidance on 𝐖𝐇𝐄𝐑𝐄 and 𝐇𝐎𝐖 to add them
→ Learning resources for gaps you need to fill

𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐜𝐫𝐚𝐳𝐲 𝐩𝐚𝐫𝐭:

I built this entire app on Replit by typing what I wanted in plain English. No coding required.

𝐑𝐞𝐩𝐥𝐢𝐭'𝐬 AI Integrations gave me instant access to OpenAI, Anthropic, Gemini, all the top models. No API keys. No setup headaches. Just picked one and started building.

𝐖𝐡𝐚𝐭 𝐑𝐞𝐩𝐥𝐢𝐭 𝐡𝐚𝐧𝐝𝐥𝐞𝐝:
→ Turned my description into working code
→ Set up AI integration automatically
→ Deployed it instantly, ready to share

This is vibe coding. You describe it. Replit builds it. You ship it.

New year, new satisfying way to solve your own problems.

Comment "𝐑𝐞𝐬𝐮𝐦𝐞" and I'll send you the link to try it yourself.

𝐘𝐨𝐮 𝐜𝐚𝐧 𝐮𝐬𝐞 𝐦𝐲 𝐥𝐢𝐧𝐤 𝐭𝐨 𝐠𝐞𝐭 $10 𝐜𝐫𝐞𝐝𝐢𝐭𝐬: https://lnkd.in/dywbyxB9

(It is eligible for people who purchased Replit Core.)

♻️ Repost if someone in your network is job hunting in 2026

I am hosting a free live workshop on building real-world data analytics projects with Chris.

Most aspiring analysts know they need a portfolio. But very few know what actually impresses hiring managers.

I've reviewed hundreds of portfolios. Most look the same: Titanic, Iris, MNIST. Tutorial follow-alongs with no business context.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐰𝐞'𝐥𝐥 𝐜𝐨𝐯𝐞𝐫:

→ Why portfolios matter and what hiring managers actually look for
→ How to choose the right projects for data analyst roles
→ How to structure an end-to-end analytics project
→ How SQL, Tableau, Python, and spreadsheets work together in real workflows
→ How to tell a compelling story with data

𝐋𝐢𝐯𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐁𝐮𝐢𝐥𝐝:

In the second half, Chris Perry and I will build a complete analytics project live:
→ Data ingestion and preparation
→ Analysis using SQL/Python
→ Visualization and dashboarding
→ Explaining every decision along the way

This is not a theory-heavy session. You'll see real tools, real workflows, and real decision-making, exactly how analytics is practiced in the industry.

𝐃𝐞𝐭𝐚𝐢𝐥𝐬:
📅 Saturday, January 17
⏰ 12:00 PM – 2:00 PM EST

𝐖𝐡𝐨 𝐭𝐡𝐢𝐬 𝐢𝐬 𝐟𝐨𝐫:
→ Aspiring and early-career data analysts
→ Career switchers breaking into analytics
→ Analysts who want to level up their portfolio

Your portfolio is your proof of work. Make it count.

𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐡𝐞𝐫𝐞: https://luma.com/q0sezwmh

Are you joining?

♻️ Repost if someone in your network is building their data analytics portfolio

Perfect analysis. Beautiful dashboard. Zero impact. Sound familiar?

Not because the data is wrong. Because the visualization quietly misleads, overwhelms, or confuses.

Here are 6 data visualization mistakes that make stakeholders ignore your insights, even when the numbers are correct 👇

𝟏. 𝐒𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐛𝐚𝐫 𝐜𝐡𝐚𝐫𝐭𝐬 𝐚𝐰𝐚𝐲 𝐟𝐫𝐨𝐦 𝐳𝐞𝐫𝐨 Makes small changes look dramatic and distorts perception.

𝟐. 𝐔𝐬𝐢𝐧𝐠 𝐩𝐢𝐞 𝐜𝐡𝐚𝐫𝐭𝐬 𝐟𝐨𝐫 𝐭𝐨𝐨 𝐦𝐚𝐧𝐲 𝐜𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐞𝐬 Human eyes can't accurately compare angles and areas.

𝟑. 𝐎𝐯𝐞𝐫𝐮𝐬𝐢𝐧𝐠 𝐜𝐨𝐥𝐨𝐫𝐬 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐦𝐞𝐚𝐧𝐢𝐧𝐠 Every extra color adds cognitive noise instead of clarity.

𝟒. 𝐃𝐮𝐚𝐥 𝐚𝐱𝐞𝐬 𝐭𝐡𝐚𝐭 𝐢𝐦𝐩𝐥𝐲 𝐟𝐚𝐥𝐬𝐞 𝐜𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬 Scale manipulation can manufacture relationships that don't exist.

𝟓. 𝐂𝐡𝐚𝐫𝐭 𝐭𝐢𝐭𝐥𝐞𝐬 𝐭𝐡𝐚𝐭 𝐝𝐞𝐬𝐜𝐫𝐢𝐛𝐞, 𝐧𝐨𝐭 𝐜𝐨𝐧𝐜𝐥𝐮𝐝𝐞 If the title doesn't state the insight, readers must guess.

𝟔. 𝐒𝐡𝐨𝐰𝐢𝐧𝐠 𝐚𝐥𝐥 𝐝𝐚𝐭𝐚 𝐢𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐭𝐡𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭 Executives want conclusions, not raw tables.

The common thread? Data doesn't speak for itself. Design decisions decide what people believe.

Good visualization isn't about being fancy. It's about being honest, clear, and decisive.

If your charts don't guide attention, they invite misinterpretation.

Which one are you guilty of? 
♻️ Repost if someone in your network builds dashboards

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𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 20,000+ readers here → https://lnkd.in/dUfe4Ac6

Data Visualization Mistakes to Avoid

Stop manually extracting data from websites that don't have APIs.

There's a better way now.

As data professionals, we've all been there. You need data from a website, there's no API, and you're stuck writing brittle extraction scripts that break every time the UI changes.

I've been testing Amazon Nova Act, and it's built for exactly this problem.

𝐖𝐡𝐚𝐭 𝐢𝐭 𝐝𝐨𝐞𝐬:

Instead of writing CSS selectors and XPath queries, you tell it what to do in plain English:
→ "Go to this page and extract all the pricing data"
→ "Fill out this form with these values"
→ "Navigate through these filters and download the results"

Nova Act handles the browser automation, retries, and state. You focus on the data.

𝐖𝐡𝐲 𝐝𝐚𝐭𝐚 teams 𝐬𝐡𝐨𝐮𝐥𝐝 𝐜𝐚𝐫𝐞:

→ No more maintaining fragile Selenium scripts
→ Extract data from dashboards and portals without APIs
→ Automate repetitive data entry across multiple systems
→ Run smoke tests to validate dashboards and reports after deployments
→ Works with Python, so it fits right into your workflow

This isn't about flashy AI demos. It's about automating the boring, repetitive stuff that eats up your time. Amazon calls them "NormcoreAgents", unsexy but reliable automation that actually works at scale.

𝐇𝐨𝐰 𝐭𝐨 𝐭𝐫𝐲 𝐢𝐭:

1. Start in the web playground (no setup needed)
2. Build your agent using natural language
3. Download the Python script
4. Customize in your IDE

𝐓𝐫𝐲 𝐨𝐮𝐭 𝐍𝐨𝐯𝐚 𝐀𝐜𝐭 → https://fandf.co/4q3jVOc

Thanks to Amazon for sponsoring this post.

What’s the most annoying website or dashboard you’ve ever had to extract data from? 👇

♻️ Repost if someone in your network spends too much time on manual data collection

#AmazonPartner

Stop opening 47 browser tabs to prep for ML interviews.

You bookmark 12 YouTube playlists. You save 8 Medium articles "for later."

Then you close your laptop and do nothing.

I've been there. Most of us have.

𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦:

ML interview prep is scattered everywhere. A bit of system design here. Some LeetCode tips there. Random probability questions somewhere else.

No structure. No roadmap. Just chaos.

𝐓𝐡𝐚𝐭'𝐬 𝐰𝐡𝐲 𝐈 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐬𝐡𝐚𝐫𝐞 𝐭𝐡𝐢𝐬 𝐆𝐢𝐭𝐇𝐮𝐛 𝐫𝐞𝐩𝐨:

It's called "Machine Learning Interview" by Khang Pham. He's a SWE/ML with 10 years of experience and got offers from 𝐆𝐨𝐨𝐠𝐥𝐞, 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧, 𝐒𝐧𝐚𝐩, 𝐚𝐧𝐝 𝐒𝐭𝐢𝐭𝐜𝐡𝐅𝐢𝐱.

The repo has 11.7k+ stars for a reason.

𝐖𝐡𝐚𝐭'𝐬 𝐢𝐧𝐬𝐢𝐝𝐞:

→ ML System Design (YouTube recommendations, LinkedIn feed ranking, ad click prediction)
→ LeetCode questions organized by category for MLEs
→ Statistics and probability cheat sheets
→ ML fundamentals (logistic regression, random forests, boosting, PCA)
→ Deep learning essentials
→ Real interview questions from FAANG companies
→ AB testing resources
→ Company-specific guides

Everything in one place. No more 47 tabs.

𝐖𝐡𝐨 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐟𝐨𝐫:

→ Data Scientists preparing for MLE roles
→ ML Engineers switching companies
→ Anyone targeting FAANG or top tech companies

The author literally documented what it takes to get offers from top companies. That's rare.

Link in comments 👇

What's your biggest challenge when prepping for ML interviews?

♻️ Repost if someone in your network is preparing for ML interviews

A model can show 99% accuracy and still be completely useless.

That's the trap imbalanced data creates.

In production, that means missed fraud. Missed failures. Missed diagnoses. Bad business decisions.

When one class dominates, your model learns to be lazy, predicting the majority every time and still looking "accurate" on paper.

This post breaks down what actually works when your data isn't balanced.
𝐒𝐭𝐚𝐫𝐭 𝐡𝐞𝐫𝐞 (𝐪𝐮𝐢𝐜𝐤 𝐟𝐢𝐱𝐞𝐬):

→ 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐜𝐨𝐬𝐭 𝐟𝐢𝐫𝐬𝐭, 𝐧𝐨𝐭 𝐝𝐚𝐭𝐚 𝐛𝐚𝐥𝐚𝐧𝐜𝐞 False positives and false negatives don't hurt equally. Optimize for the mistake you can't afford.

→ 𝐂𝐥𝐚𝐬𝐬 𝐰𝐞𝐢𝐠𝐡𝐭𝐬 𝐛𝐞𝐟𝐨𝐫𝐞 𝐚𝐧𝐲𝐭𝐡𝐢𝐧𝐠 𝐟𝐚𝐧𝐜𝐲 A 30-second change that works surprisingly often.

→ 𝐅𝐢𝐱 𝐲𝐨𝐮𝐫 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐦𝐞𝐭𝐫𝐢𝐜𝐬 Accuracy hides failure. Precision, recall, F1, and confusion matrices don't.

𝐖𝐡𝐞𝐧 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐦𝐨𝐫𝐞 𝐟𝐢𝐫𝐞𝐩𝐨𝐰𝐞𝐫:

→ 𝐒𝐌𝐎𝐓𝐄, 𝐮𝐬𝐞𝐝 𝐜𝐚𝐫𝐞𝐟𝐮𝐥𝐥𝐲 Synthetic samples help, but only on training data. Never test data.

→ 𝐔𝐧𝐝𝐞𝐫𝐬𝐚𝐦𝐩𝐥𝐢𝐧𝐠 𝐭𝐡𝐞 𝐦𝐚𝐣𝐨𝐫𝐢𝐭𝐲 Sometimes throwing data away makes models faster and better.

→ 𝐄𝐧𝐬𝐞𝐦𝐛𝐥𝐢𝐧𝐠 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐛𝐚𝐥𝐚𝐧𝐜𝐞𝐝 𝐝𝐚𝐭𝐚𝐬𝐞𝐭𝐬 More compute, but consistently stronger results than single resampling.

→ 𝐑𝐞𝐟𝐫𝐚𝐦𝐞 𝐚𝐬 𝐚𝐧𝐨𝐦𝐚𝐥𝐲 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 Stop classifying when "rare" truly means "abnormal."

𝐌𝐲 2 𝐜𝐞𝐧𝐭𝐬: There is no universal solution. Some problems are solved with class weights. Others need ensembles. Sometimes the only honest answer is more minority data, boring, slow, but effective.

If you work on fraud, churn, risk, defects, or rare-event prediction, this is one of those topics that quietly decides whether your model helps or harms.

What else would you add?

♻️ Repost if someone in your network works with imbalanced datasets

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Handling Imbalance Data

5 things I wish I knew before starting my Data Science career (would have saved me a year!)

I learned these the hard way. You don't have to.

𝟏. 𝐒𝐐𝐋 > 𝐅𝐚𝐧𝐜𝐲 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬
↳ I spent months learning complex ML models
↳ Meanwhile, 80% of my job was writing SQL queries
↳ Master the fundamentals first. They never go out of style.

𝟐. 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 > 𝐂𝐨𝐝𝐞
↳ Your model is useless if stakeholders don't understand it
↳ Learn to explain complex things simply
↳ The best data scientists are translators, not just coders

𝟑. 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 > 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐞𝐬
↳ I collected certificates like trophies
↳ Hiring managers barely glanced at them
↳ A solid portfolio speaks louder than any credential

𝟒. 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐦𝐩𝐚𝐜𝐭 > 𝐌𝐨𝐝𝐞𝐥 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲
↳ Nobody cares if your model is 95% accurate
↳ They care if it saved money or drove revenue
↳ Always tie your work back to business outcomes

𝟓. 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐢𝐧𝐠 > 𝐀𝐩𝐩𝐥𝐲𝐢𝐧𝐠 𝐁𝐥𝐢𝐧𝐝𝐥𝐲
↳ My best opportunities came through connections
↳ Not from submitting 100+ applications
↳ Build relationships before you need them

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬:
The technical stuff is the easy part. The career stuff? That's where most people struggle.

Focus on skills that compound over time: communication, business thinking, and relationships.

Which of these hit home for you? 👇

♻️ Repost to help someone starting their data career

𝐏.𝐒. I share insights on data analytics & career growth in my free newsletter. Join 21,000+ here → https://lnkd.in/dUfe4Ac6

I've watched 3-week analyses get ignored in 3-minute meetings


You can build the cleanest dashboard. Run the most advanced analysis. And still… nothing changes.

Not because the numbers are wrong. Because the right business questions were never asked.

𝐁𝐞𝐟𝐨𝐫𝐞 𝐲𝐨𝐮 𝐬𝐭𝐚𝐫𝐭:

→ What decision will this influence?
If no decision changes, the analysis adds no real value.

→ Who is the decision maker?
Insights need an owner; otherwise, they die in slides.

→ Why does this problem matter now?
Timing defines relevance more than sophistication.

→ What would success look like?
Without clear success criteria, results are meaningless.

𝐃𝐮𝐫𝐢𝐧𝐠 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬:

→ What action will be taken if numbers change?
Metrics without actions create dashboards, not outcomes.

→ What assumptions are we making?
Unchallenged assumptions lead to confident mistakes.

→ What is the cost of being wrong?
False positives and false negatives are not equal.

→ What data do we actually need?
More data often adds noise, not clarity.

𝐁𝐞𝐟𝐨𝐫𝐞 𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐢𝐧𝐠:

→ What context is missing from the numbers?
Seasonality, market shifts, and policy changes change everything.

→ How will this insight be communicated?
If it can't be explained simply, it won't be used.

Good analysts don't start with data. They start with decisions, actions, and consequences.

For me, the most skipped question is "What decision will this influence?" I've seen entire projects die because no one asked it upfront.

Which one do you see skipped most often?

♻️ Repost if someone in your network works with data

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📚 Get 150+ real interview questions (with solutions & frameworks) in our Data Analyst Interview Prep Book: https://lnkd.in/dyzXwfVp

𝐏.𝐒. I share insights on data analytics & career growth in my free newsletter. Join 20,000+ readers here → https://lnkd.in/dUfe4Ac6

I just analyzed 1,000+ job postings for data roles in 2026. Here's what I found.

(The market has completely shifted.)

Companies aren't hiring the way they used to. Welcome to the "precision hiring" era. Fewer roles, higher expectations, bigger paychecks for specialists.

I used Superagent, a newly launched AI product from Airtable, to research this. Right away, multiple specialized AI agents worked simultaneously to pull from sources like LinkedIn Jobs, Glassdoor, and industry reports, to deliver an interactive 𝐒𝐮𝐩𝐞𝐫 𝐑𝐞𝐩𝐨𝐫𝐭 with salary benchmarks, skill trends, and hiring patterns.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐬𝐡𝐨𝐰𝐬:

𝟐𝟎𝟐𝟔 𝐒𝐚𝐥𝐚𝐫𝐲 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐬 (𝐌𝐢𝐝𝐩𝐨𝐢𝐧𝐭):
↳ Data Analyst: $117,250 (+3.3% YoY)
↳ Data Scientist: $153,750 (+4.1% YoY)
↳ Data Engineer: ~$145,000
↳ ML Engineer: $170,750 (+4.4% YoY)

𝐓𝐡𝐞 𝐒𝐤𝐢𝐥𝐥𝐬 𝐒𝐡𝐢𝐟𝐭:
↳ 60% of Data Scientist postings now require AI skills
↳ LLM experience is the #1 demanded AI skill
↳ Python + SQL are still non-negotiable
↳ Cloud platforms (AWS, Snowflake) showing up in 41% of DS roles

𝐁𝐢𝐠𝐠𝐞𝐬𝐭 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬:
→ ML Engineer is the #1 fastest-growing role in the US (per LinkedIn)
→ Entry-level roles are shrinking. Companies want production-ready skills.
→ AI-skilled professionals command 5-12% salary premiums
→ The market isn't saturated, it's "misaligned". Employers want judgment, not just theory.

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬:
The days of learning Python and landing a data job are fading. 2026 rewards specialists who can ship end-to-end, work with AI tools, and make decisions under uncertainty.

If you're job hunting this year, focus on production experience over certifications.

If you want to check out the full Super Report I generated, comment “𝐑𝐞𝐩𝐨𝐫𝐭” below and I'll send it to you. The full report includes interactive salary charts, geographic breakdowns, and top hiring companies. All with citations you can actually verify

𝐓𝐫𝐲 𝐢𝐭: https://lnkd.in/dBMd5wTU

Use my code "VENKATA2FREE" to get 2 months free for Superagent!

Which role are you targeting in 2026? 👇

♻️ Repost to help someone plan their data career this year

#SuperagentPartner
Airtable Superagent

Brand partnership • Data Scientist | 3+ years in Predictive Analytics, Experimentation & Business Impact | 200K+ Data Community | Featured on Times Square, Fox, NBC

I'm hosting a FREE workshop on how to ACE your next data interview.

(Stop tweaking your resume. Start building proof.)

Most candidates fail data interviews not because they lack skills, but because they don't know what to focus on or how to show it.

In this session, I'm breaking down the exact roadmap hiring managers expect in 2026:

𝐖𝐡𝐚𝐭 𝐰𝐞'𝐥𝐥 𝐜𝐨𝐯𝐞𝐫:
→ The full interview process (what actually happens)
→ SQL exercises with real practice problems
→ Business sense questions and how to answer them
→ Behavioral interview frameworks that work
→ The What, Why, How framework for any question

This is for you if:
↳ You're preparing for data analyst or data scientist interviews
↳ You keep getting rejected without knowing why
↳ You want a clear, practical prep plan, not random LeetCode grinding

𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐡𝐞𝐫𝐞: https://luma.com/yic2lm38

See you there 👋

♻️ Repost to help someone preparing for their next data interview

—

📚 Get 150+ real interview questions (with solutions & frameworks) in our Data Analyst Interview Prep Book: https://lnkd.in/dyzXwfVp

𝐏.𝐒. I share insights on data analytics & career growth in my free newsletter. Join 21,000+ readers here → https://lnkd.in/dUfe4Ac6
Activate

More than 400 people registered for our Bay Area Data & AI meetup.

1 day left to join us.

This is a casual gathering for people working in data, AI, and product. We'll talk about trends in data, how roles are shifting, the evolution of skills, network, and swap stories about what we're excited about.

𝐃𝐞𝐭𝐚𝐢𝐥𝐬:
📅 Saturday, January 24
⏰ 10:00 AM PST

𝐇𝐨𝐬𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐦𝐞:
→ Manisha Arora
→ Rami Krispin
→ Vishakha Sadhwani
→ Sriram Arumelli
→ Devshree Bharatia

If you're navigating the AI shift in data roles, this is the room to be in.

𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐡𝐞𝐫𝐞: https://luma.com/kgsx6o61

Thanks to Findzo for sponsoring this event! They help teams spot where AI conversations are failing, like identifying why a support bot's escalations were spiking and what to fix. Check them out → https://www.findzo.ai

See you all tomorrow 👋

♻️ Repost if someone in your network works in data or AI in the Bay Area

AI can write code in seconds. Shipping it still takes hours.


Claude Code, Windsurf, Curso, the demos look incredible. But here's what nobody talks about: the last mile.

You can generate beautiful code. Build something that works locally. And then… it just sits there. On your machine. Unseen. Unshipped.

Not because the code is bad. Because shipping is still a pain.

𝐓𝐡𝐞 𝐠𝐚𝐩 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 "𝐢𝐭 𝐰𝐨𝐫𝐤𝐬" 𝐚𝐧𝐝 "𝐢𝐭'𝐬 𝐥𝐢𝐯𝐞":

→ Setting up hosting, Configuring databases
→ Managing secrets and environment variables
→ Deploying without breaking everything
→ Making it shareable with a real URL

This is where most AI-built projects die.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐈'𝐯𝐞 𝐛𝐞𝐞𝐧 𝐝𝐨𝐢𝐧𝐠:

I use 𝐂𝐥𝐚𝐮𝐝𝐞 𝐂𝐨𝐝𝐞 for the deep work, writing logic, refactoring, working through complex code. It's powerful for that.

But when it's time to ship? I move to 𝐑𝐞𝐩𝐥𝐢𝐭.

→ Runs in the browser. No local setup headaches.
→ Databases, secrets, and storage are built-in.
→ One-click deploy. Real URL. Actually live.
→ Works with Claude Code through GitHub sync.

Claude Code handles the thinking. Replit handles the shipping.

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬: The best AI coding setup isn't about picking one tool. It's about combining the right ones. Claude Code for control. Replit for getting it out the door.

If your projects keep dying on localhost, this combo might be worth trying.

What's your shipping workflow? Drop it below.

𝐘𝐨𝐮 𝐜𝐚𝐧 𝐮𝐬𝐞 𝐦𝐲 𝐥𝐢𝐧𝐤 𝐭𝐨 𝐠𝐞𝐭 $10 𝐜𝐫𝐞𝐝𝐢𝐭𝐬: https://lnkd.in/dywbyxB9

(It is eligible for people who purchased Replit Core.)

♻️ Repost if someone in your network builds with AI

Data scientists won't be replaced by AI. They'll be replaced by data scientists who know how to use AI.

I've seen it firsthand. Data scientists who were "𝐬𝐞𝐧𝐢𝐨𝐫" two years ago are now competing with juniors who know how to automate workflows with AI.

If you're still doing manual EDA, writing the same SQL queries, and building dashboards one at a time, you're already behind.

That's why I'm recommending the 𝐅𝐮𝐭𝐮𝐫𝐞𝐏𝐫𝐨𝐨𝐟 𝐀𝐈 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐁𝐨𝐨𝐭𝐜𝐚𝐦𝐩 (Jan 2026 Cohort) by Andres Vourakis with 7 years of experience in AI/ML.

This isn't another "intro to ChatGPT" course. It's a 6-week deep dive into designing and automating workflows that 10x your impact.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮'𝐥𝐥 𝐥𝐞𝐚𝐫𝐧:

→ Semantic Layers & Data-Aware AI (build text-to-SQL that actually works)
→ AI Coding Environments & MCP (set up Cursor for production-ready development)
→ Agents vs Workflows (know when to use each, build with LangGraph)
→ EDA Agent & A2A Protocol (automate exploratory analysis)
→ System Design for Agentic Systems (break down AI into clear, logical components)
→ Capstone: Build a Talk-to-Your-Data Slackbot from scratch

𝐖𝐡𝐚𝐭 𝐲𝐨𝐮'𝐥𝐥 𝐛𝐞 𝐚𝐛𝐥𝐞 𝐭𝐨 𝐝𝐨 𝐚𝐟𝐭𝐞𝐫:

→ Design AI workflows that handle repetitive data tasks
→ Build agents that surface insights automatically
→ Ship production-ready tools your stakeholders can actually use
→ Stand out in interviews with real agentic projects

𝐃𝐞𝐭𝐚𝐢𝐥𝐬:
📅 Jan 23 - Mar 5 (6 weeks)
🎟️ Use code 𝐉𝐀𝐍𝟐𝟔-𝟐𝟓-𝐒𝐀𝐈 for 25% off
⏰ Enrollment closes January 19, 2025

𝐄𝐧𝐫𝐨𝐥𝐥 𝐡𝐞𝐫𝐞 → https://futureproofds.com/

𝐌𝐲 2 𝐜𝐞𝐧𝐭𝐬: This bootcamp teaches you to be the one doing the replacing, not the one getting replaced.

Are you leveling up your AI workflow skills this year?

♻️ Repost if someone in your network is in data or AI

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Vitalii Dodonov

Venkata Naga Sai Kumar Bysani

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Performance ScoreVenkata Naga Sai Kumar Bysani's LinkedIn Performance

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The Battle

Vitalii Dodonov

Venkata Naga Sai Kumar Bysani

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Vitalii Dodonov's LinkedIn Performance

Performance ScoreVenkata Naga Sai Kumar Bysani's LinkedIn Performance

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Performance ScoreVitalii Dodonov's LinkedIn Performance