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

My dad is a 10-time world champion in skydiving.

It's a dangerous sport. 
Most teams don’t train in bad weather.

He made his team train in near-storm conditions —

because you never know what the weather will be on competition day.

His results speak for themselves.

He taught me to show up — and act — no matter how scared you are.

That mindset has been the backbone of everything I’ve achieved.

What did you learn from your parents?

li_at

JSESSIONID

Talks about #startup, #founders, #leadership, #engineering, and #enterprenuership

Today, Nadine leads 50 people at Stan — after rejecting my job offer 3 times.

Two years ago, she was a contract support agent.

I’d get daily performance reports from Alexa — 
We pushed everyone to hit 60+ emails per day.

While most people struggled,
Nadine never missed a day. Always above target.

When I asked her to join full-time, she said no.
“San Diego. My husband’s job. We love the mountains.”

Six months later, another walk down Venice Beach.

"I'd love you to join Stan full-time."
"San Diego... my husband... Let me think about it."

"Is this a hard no from your family?"
"Well... I can be quite convincing."
"Good to know!"

At our Cabo offsite, Alexa said:
"V, I need a people manager. Nadine would be perfect — but she won't move."

"She told me she can be convincing. Let’s give her a reason to be."

So I went to Nadine one more time.

"I know San Diego keeps you there.
But I need your help here.

In the end, it’s either you supporting the family 
or the family supporting you.

Let me make supporting you the best decision for your family."

Alexa came with the offer.

Turns out Nadine can indeed be very convincing 
(thank you Garrett for supporting her! 🙏).

Within a year, she took over as Head of Support 
(when I asked Alexa to build a new org with me)

Thank god we didn't give up.
The best talent is always worth the wait.

Head of Creator Support @ Stan I MA Organizational Leadership

Stan

People Ops @ Stan

When you work - work. When you rest - rest.

Don't mix the two.

Last week I caught myself texting during our product meeting.

Then I zoned out on Slack for 10 minutes at dinner 
(mrs wasn't happy 😅).

It's human to want everything at once.

But here's what building Stan taught me:

The best ideas come from meetings where phones are face down.
The best conversations at home happen when Slack is off.

When you try to be everywhere, you're nowhere.

I spent years thinking "always on" showed dedication.
All it showed was lack of focus.

Your ability to drive change comes from being 100% present.
Not 70% in three places (that wastes time in 3 places).

I still slip up - often.

But when I do get it right, it’s like life slows down just enough to notice what matters.

How’s your relationship with presence lately?

My first YouTube video just went live!!! 🎬🚀

Taking y’all behind the scenes of our 14-day SF Hacker House. 

Where me and 8 of my best builders at Stan launched 2 AI products live.

Tune in if you’re into startups + building in public, or just wanna see how we build here at Stan. 

Watch here: https://lnkd.in/gNqVTEBR

I rewired my personality in 2 years. 

ISTJ → INTJ.

Yes, you can train intuition.

In 2022, I took the Myers-Briggs test on 16Personalities.
It said ISTJ — the Logistician.

I looked up the types of my heroes:

Elon Musk: INTJ
Steve Jobs: INTJ (or ENTP)
Jensen Huang: ENTJ
Mark Zuckerberg: INTJ
Jeff Bezos: ENTJ

Every world-shaping entrepreneur had one letter I didn't: N.

So I studied them.
Turns out, almost all are habitual readers — especially of science fiction.

This is where vision comes from:

1️⃣ Imagining a world that doesn’t exist
2️⃣ Envisioning a path toward it
3️⃣ Having the courage to start walking

The following year, I read 20+ sci-fi books — whatever they read, I wanted to read.

Some of my favourites:

1/ Foundation (5 books)
2/ I, Robot (7 books)
3/ Hitchhiker’s Guide to the Galaxy
4/ Tomorrow, and Tomorrow, and Tomorrow
5/ Ender’s Game

📚 Reading rewired how I think.

Today, my MBTI is INTJ — 76% Intuition.
(Q for my team @Stan: did you notice?)

I’m sharing this story because: 
You can change how your mind works — if you choose to.

YOU CAN CHANGE.

When the destination is clear, 
The path unveils itself. 

What’s your Myers-Briggs type?👇

How I spot top 1% talent in one question.

After 100+ interviews building a $30M ARR team, I’ve boiled it down to one question:

“What have you built?”

Very few can answer it with the kind of depth that makes it undeniably clear they know what they did, why it worked, and the impact it drove.

When I find myself leaning forward, asking “Wait, how did you figure that out?” 

I know I’m talking to someone different.

Because most people work on things.
The top 1% build things.

If you’re a founder hiring - how do you spot the top 1%?

One thing I've been subconsciously lucky about —

Living where it's sunny. ☀️

I never realized how much it shaped my energy, focus, and optimism.

Until I spent a few weeks somewhere gray.
Suddenly, everything felt heavier.

It's strange how the weather can change your will to act.

With sunlight, small things feel doable.
Without it, even easy things feel like effort.

Most people underestimate how much their environment drives them.

We think it's our discipline or motivation —
But sometimes, it's just that strike of yellow on your table
Breaking through the window of a coffee shop.

If you ever feel stuck, it might not be you.
It might be the weather.

P.S. Have you noticed this too? 
Where do you live — and how's the sun treating you?

This is me building our ’26 strategy right now.

Our #1 problem:

We outgrew founder-led execution faster than we scaled our team to execute on our behalf.

As we scaled the business to $30M ARR in 3 years,

We fell behind on scaling ourselves.

2026 is going to be the evolution on how we run the company. 

My job as a Founder:
Build a machine that builds the machine.

A measure of success I’ll hold myself accountable to:
✅ Was this done without me?

—
Founders Q: What broke when you finally stepped back?

Someone recently asked me how we priced Stan at $29/mo.

Now we're a $30M ARR business.

All I said was: 🤷‍♂️ 😅

We needed to pick a number, so we did.

Our only compass was wanting to build a premium product. So we picked a premium-ish price and shipped it.

And that's what I want to share today:

No one knows what they're doing.
Even the top 1%.

In retrospect, we all have playbooks explaining why the thing we did worked and how exactly it's brilliant.

But in practice...

Every single top performer I've met is just making educated guesses and hoping for the best.

They favour action over inaction, and they trust their gut.

In the end,
Results take care of themselves.

--

Do you also feel like you don't know what you're doing, sometimes? 🤔

Your first 100 customers will bring your next 100.

For free.

If they don’t, your product’s not good enough yet.

We went all-in on those first 100.
1:1 coaching.
On-the-call bug fixes.
Feature requests shipped overnight.

We obsessed over each one until they felt their version of:
“I can’t believe you did that.”

When you exceed expectations like that, word spreads faster than anything you can pay for.

Most founders overlook this part - and that’s a mistake.

If you’re not texting your first 100 customers,
you’re not doing enough.

P.S. Four years and $30M ARR later - we’re still texting.

Working out of a NYC WeWork this week — and remembering the good old days. 🗽

We built the first $10M of Stan out of WeWorks between LA and Toronto.

I still remember hacking away at that 8-person WeWork table.

Back then, we’d just walk out to a nearby café for lunch.

Now, coordinating lunch across our 25-person offices is a full-blown operation.

Feeling nostalgic ☺️

P.S. WeWork kombucha is still unmatched.

In October, I flew my team to SF to build 2 AI projects in public.

Our documentary is LIVE 🟢

We built:
1/ A pivotal feature for Stanley (Your Content Coach) 
2/ The foundation for our company pivot 

(yep, at $30M ARR!)

We filmed the entire trip.

226 hours of footage → 30 minutes of building in public documentary.
(Day 2 pivot? Yep!)

Why watch:

🍎 Experienced founders: innovation beyond our core.
🚀 First-time founders: copy our 14-day launch playbook!
🫂 Anyone who wants to work at Stan - this is how we build.

Watch here (or save for later): https://lnkd.in/gNqVTEBR

P.S. If you want to see why building is an iterative process, watch me at 13:42 trying to build a whiteboard 😅

Stanley (Your Content Coach)

We fail in business for the same reason we fail at habits:

Doing too much 🤹‍♀️

It takes about 70 repetitions to form a new neural pathway —
to make an action automatic.

But somewhere along the way, we get excited.
We add a new habit.
Now the first one gets only half our attention.

The new neural pathways are still fragile —
and without reinforcement, they fade.
The habit never sticks.

The same happens in business.
We rarely stay with something long enough for it to reach its full potential.

Imagine how different your results would look
if you finished just one thing all the way through.

What is that thing for you?

Stanley is behind 2.1x engagement for 1,267 Entrepreneurs on LinkedIn.

Instagram, you're next 📲

6 months ago, we launched Stanley for LinkedIn. 
Now serving 1,267 entrepreneurs.

In just 30 days, they see: 
• 2.1x avg. engagement increase 
• 11% more posts 
• 131% growth in comments

What did we do?

We taught Stanley to speak Instagram! 
(yep, he can now watch videos)

𝗪𝗵𝗼 𝗶𝘁'𝘀 𝗳𝗼𝗿: 
• Aspiring Entrepreneurs on IG (1-10K) 
• Still finding your voice 
• Need help growing your followers

𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗱𝗼? 
• Watch all your content 
• Find patterns (so growth isn't random) 
• Send personalized content ideas 
• Script in your voice 
• Help stay consistent

𝗢𝘂𝗿 𝗽𝗿𝗼𝗺𝗶𝘀𝗲:
Your stories deserve to be heard. 
Stanley makes sure they are. 
While helping you grow 3x faster.

𝗪𝗵𝘆 𝗻𝗼𝘄?
$19/mo Early Adopter + 14 days free trial.

Tomorrow, we're back to $47/mo once Cyber Monday is over 

Get your spot here: https://lnkd.in/gKqxnFUK

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 with a passion for driving data-driven strategies, predictive modeling, and team optimization across various industries, including insurance and retail. 

Currently, I lead data initiatives at BCBS of SC, where I develop dashboards, optimize workflows, and build predictive models to support C-Suite decision-making and business strategies. My work spans multiple tools like SQL, Python, AWS, and Power BI to streamline processes and enhance operational efficiency.

Career Highlights:

•	Optimized workflows, saving 120+ hours of manual work per week, driving efficiency through AWS integration.
•	Developed real-time dashboards at The LEGO Group, improving sales forecasting accuracy by 15% and decision-making speed by 23%.
•	Recognized for building predictive models, earning recognition in data science competitions like NESS Statathon (3rd place out of 150 participants).

Beyond my work, I've had the privilege of competing and winning several hackathons and data science competitions, including earning 1st prize at Well4Tech’s Data Science Health Hackathon, and NESS Statathon by Travelers. My innovative work has been featured on Times Square billboards, and covered by major media outlets like Fox, NBC, and over 20+ articles. Additionally, I’ve been recognized by Favikon for my contributions to the data community.

I have experience working with a range of data science tools like PySpark, SQL, Power BI, and Python, and hold certifications such as Tableau Desktop Specialist and AWS Machine Learning. As a strong believer in mentorship and continuous learning, I’ve also contributed as a Graduate Teaching Assistant for SQL and led the UConn Data Analytics Club, fostering growth and development within the community.

Feel free to reach out if you’d like to discuss data analytics, data science, or potential collaboration: saikumarbysani149@gmail.com.

💬 𝗩𝗶𝗲𝘄𝘀 𝗮𝗿𝗲 𝗺𝘆 𝗼𝘄𝗻 𝗮𝗻𝗱 𝗱𝗼 𝗻𝗼𝘁 𝗿𝗲𝗳𝗹𝗲𝗰𝘁 𝘁𝗵𝗼𝘀𝗲 𝗼𝗳 𝗺𝘆 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗿!

Venkata Naga Sai Kumar Bysani

6 books that will make you a 10x data analyst:


𝟏. 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐃𝐚𝐭𝐚 - Cole Nussbaumer Knaflic
Teaches the mindset shift of turning clear, actionable stories that drive decisions.

𝟐. 𝐋𝐞𝐚𝐧 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 - Croll & Yoskovitz
A must-read on picking the right metrics and using data to drive growth and decisions.

𝟑. 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 - Provost & Fawcett
Builds intuition for how analytical methods connect to business strategy and ROI.

𝟒. 𝐓𝐡𝐞 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐓𝐨𝐨𝐥𝐤𝐢𝐭 - Ralph Kimball
Foundational for understanding dimensional modeling and answering systems.

𝟓. 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 - Wes McKinney
The ultimate guide to using Pandas & NumPy, written by the creator of Pandas himself.

𝟔. 𝐍𝐚𝐤𝐞𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - Charles Wheelan
Makes statistics approachable and shows when data truly matters (and when it's misleading).

𝐖𝐡𝐚𝐭 𝐞𝐥𝐬𝐞 𝐬𝐡𝐨𝐮𝐥𝐝 𝐦𝐚𝐤𝐞 𝐭𝐡𝐢𝐬 𝐥𝐢𝐬𝐭?

——

Join my newsletter with 19,000+ data professionals:
→ https://lnkd.in/dUfe4Ac6

———

♻️ Repost to help others become better data analysts.
👤 Follow Sai Kumar Bysani + turn on notifications.

87% of data science projects never make it to production.

Bad project structure is a big reason why.

Steal this production-ready template 👇

1️⃣ 𝗰𝗼𝗻𝗳𝗶𝗴/ → Config files
• Separate parameters from code
• local.yaml for dev, prod.yaml for deployment

2️⃣ 𝗱𝗮𝘁𝗮/ → Data lifecycle
• 01-raw/ → raw source data
• 02-preprocessed → cleaned datasets
• 03-features/ → engineered features
• 04-predictions/ → model outputs

3️⃣ 𝗲𝗻𝘁𝗿𝘆𝗽𝗼𝗶𝗻𝘁/ → Application scripts
• train.py → orchestrates the full training pipeline
• inference.py → runs batch or real-time predictions

4️⃣ 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀/ → Exploration only
• For analysis & EDA, 𝗻𝗼𝘁 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗹𝗼𝗴𝗶𝗰

5️⃣ 𝘀𝗿𝗰/ → Core code & ML pipelines
• Modular code: feature engineering, training, inference
• Reusable, testable, and configurable

6️⃣ 𝘁𝗲𝘀𝘁𝘀/ → Automated checks
• Verify pipelines and edge cases
• Prevent silent failures after refactoring

7️⃣ 𝗱𝗼𝗰𝗸𝗲𝗿 & 𝗲𝗻𝘃 𝗳𝗶𝗹𝗲𝘀 → Reproducibility
• Consistent setup across machines and CI/CD

8️⃣ 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝗶𝗲𝘀 → Locked dependencies
• Ensure exact versions and reproducible results

Structure it right once, save yourself hours later.

♻️ Repost if you know someone building their data science portfolio

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

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

Thanks for making it easier for the audience, Timur!!

If you're preparing for a data analyst/data science role (in 1 month or less), you must read these case studies!

1. 𝐀𝐢𝐫𝐛𝐧𝐛'𝐬 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐭 𝐒𝐜𝐚𝐥𝐞 
→ Built Minerva to democratize data access for 5000+ employees 
→ Reduced data incidents by 90% with proactive monitoring
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dhnfPCTT

2. 𝐍𝐞𝐭𝐟𝐥𝐢𝐱'𝐬 𝐀/𝐁 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 
→ Run 1000+ experiments simultaneously for 230M+ subscribers 
→ Statistical rigor with automated analysis pipelines
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dsBsHNqT

3. 𝐏𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭'𝐬 𝐕𝐢𝐬𝐮𝐚𝐥 𝐒𝐞𝐚𝐫𝐜𝐡 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 
→ Processes 250M+ visual searches monthly with sub-second latency 
→ Increased engagement by 42% for related searches
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dFxS_RWh

4. 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧'𝐬 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 
→ Powers 800M+ member profiles with Pro-ML platform 
→ Frame feature store ensures consistency across training & serving
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/d27PeXEY

5. 𝐒𝐥𝐚𝐜𝐤'𝐬 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐌𝐞𝐬𝐬𝐚𝐠𝐢𝐧𝐠 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 
→ Handles 2B+ messages daily with <500ms global delivery 
→ Scales to 16M channels per host with consistent hashing
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/d8ZYqFzX

6. 𝐋𝐲𝐟𝐭'𝐬 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐏𝐫𝐢𝐜𝐢𝐧𝐠 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 
→ Uses online reinforcement learning for real-time price optimization 
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/deienatS

7. 𝐔𝐛𝐞𝐫'𝐬 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 
→ Process 500 billion events daily with sub-second query response 
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dsVb_XHP

8. 𝐒𝐩𝐨𝐭𝐢𝐟𝐲'𝐬 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐄𝐧𝐠𝐢𝐧𝐞 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 
→ Personalize music for 500M+ users using collaborative filtering + deep learning 
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dhvgjCwD

9. 𝐃𝐨𝐨𝐫𝐃𝐚𝐬𝐡'𝐬 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 
→ Democratize A/B testing across all teams with multi-sided experiments → Scale from 10 to 1000+ experiments per quarter
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dnt-ipbe

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐬𝐡𝐨𝐮𝐥𝐝 𝐟𝐨𝐜𝐮𝐬 𝐨𝐧: 
- What metrics and KPIs drove business decisions 
- How they handled data quality and governance 
- Statistical methods and ML algorithms chosen 
- Tools and tech stack for specific use cases

Don't get distracted by fancy algorithms; start with the business problem. Most successful analytics projects solved core issues with clear metrics and thoughtful experimentation.

Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp

♻️ Save it for later or share it with someone who might find it helpful!

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

I built an AI agent that turns resumes into pitch decks in 30 seconds. And it's already helping job seekers stand out from 200+ applicants.

You spend hours perfecting your resume. Send it to dozens of companies. And still get ghosted.

Not because you're not qualified.
But because you're playing the same game as everyone else.

While others attach PDFs, what if you sent a personalized pitch deck instead?

𝐇𝐞𝐫𝐞'𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬:
→ Upload your resume + job description
→ AI analyzes and matches your skills to their needs
→ Generates stunning pitch deck with Gamma API
→ Automatically emails you the deck
→ Attach it to recruiters, hiring managers, interviewers

Comment "𝐆𝐚𝐦𝐦𝐚" and I will send you:
- n8n Gamma workflow template
- 10 Gamma prompts for different roles
- 10 message templates for recruiters/hiring managers

Built this using n8n + Gamma API in a few hours.

The technical stack is simple, but the impact is massive.

With Gamma's API, creating presentations is now as easy as filling a form. Watch AI generate professional visuals in seconds.

Stand out from the 200+ applicants applying for the same role.

Stop sending resumes. Start sending pitch decks.

♻️ Repost this if you found this helpful

#GammaPartner

These Python commands cover 90% of data cleaning tasks you'll ever need 👇


Data cleaning is one of the most important and time-consuming steps in any data project.

To help speed things up, I've put together a cheat sheet of the most commonly used Python (Pandas) commands for cleaning, transforming, and exploring your data.

𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐢𝐭 𝐜𝐨𝐯𝐞𝐫𝐬:

𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗗𝗮𝘁𝗮:
 • df.isnull().sum() shows you where nulls are
 • df.fillna() or dropna() helps handle them
 • df.replace() swaps values cleanly

𝗜𝗻𝘀𝗽𝗲𝗰𝘁𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮:
 • df.head() and df.tail() show sample rows
 • df.info() reveals types and nulls
 • df.describe() gives numeric stats at a glance

𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗖𝗼𝗹𝘂𝗺𝗻𝘀:
 • df.rename() makes columns readable
 • df.astype() converts column types
 • df.columns.str.strip() removes whitespace

𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗻𝗴 & 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴:
 • df.loc[] and df.iloc[] for slicing
 • df.query() for custom filtering

𝗠𝗲𝗿𝗴𝗶𝗻𝗴 & 𝗚𝗿𝗼𝘂𝗽𝗶𝗻𝗴:
 • pd.merge() joins tables
 • pd.concat() stacks them
 • groupby().agg() lets you summarize

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

♻️ Share if you know someone stuck in tutorial hell

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

Save this post to save months of job search!

(Not Kidding)

Job Searching can feel endless, with hundreds of applications, ghosted emails, and constant rejections.

But here’s the thing: job searching doesn’t have to feel like a full-time job.

I’ve been testing a tool that honestly feels like cheating (the good kind): JobCopilot.

It’s like having a 24/7 job-hunting assistant that never sleeps.

𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐢𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐝𝐨𝐞𝐬:

↳  Finds relevant jobs across 500,000+ company sites
↳  Fills out the boring fields - screening Qs, applications, repetitive forms
↳  Applies for you (or saves jobs for review)
↳  Emails you a daily recap of what’s been applied to
↳  Lets you run multiple “copilots” - for different CVs, roles, even countries
↳  Learns your preferences the more you use it

If you’re tired of burnout from 50+ manual applications a day, this is the sign you needed.

Start using JobCopilot here: https://lnkd.in/dEVsCgSA

Let JobCopilot handle the heavy lifting so you can focus on:
↳  Networking
↳  Interview prep
↳  Coffee chats (the good kind of job search work)

I wish I had this tool when I was on a Job hunt!

Your next offer might literally be one automated application away.

♻️ Repost to help job seekers who need this right now.

If you're a data analyst or someone building a career in data analytics, there are 4 stages to it:

𝗦𝘁𝗮𝗴𝗲 1: "𝗟𝗲𝗮𝗿𝗻 𝘁𝗵𝗲 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀" 𝗗𝗮𝘁𝗮 𝗛𝗮𝘁
These are the skills you're expected to come with:
→ Excel + SQL basics
→ Data visualization (Tableau/Power BI)
→ Basic statistics & probability
→ Python/R fundamentals

This is the one hat every data analyst wears, no matter the company.

𝗦𝘁𝗮𝗴𝗲 2: 𝗧𝗵𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗛𝗮𝘁
Here your basics are solid, and you add the business layer:
→ Advanced SQL (CTEs, window functions, optimization)
→ ETL/ELT pipelines
→ Data warehousing concepts
→ Dashboard design & storytelling
→ Stakeholder management

This is when data analytics starts feeling real ~ and you become the "go-to person" for insights.

𝗦𝘁𝗮𝗴𝗲 3: 𝗧𝗵𝗲 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗠𝗟 𝗛𝗮𝘁
This is where multiple hats start stacking:
→ Statistical modeling & A/B testing
→ Machine learning basics (scikit-learn, feature engineering)
→ Cloud platforms (AWS/GCP/Azure for data)
→ Big data tools (Spark, Hadoop basics)
→ Real-time analytics
→ Predictive analytics & forecasting

This is the stage where you shift from "reporting what happened" to predicting what will happen ~ including building models that drive actual business decisions.

𝗦𝘁𝗮𝗴𝗲 4: 𝗧𝗵𝗲 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴-𝗘𝗹𝘀𝗲 𝗛𝗮𝘁
All the invisible work data analysts quietly own:
→ Data governance & quality
→ Cost optimization for data infrastructure
→ Mentoring junior analysts
→ Cross-functional project leadership
→ AI/ML model monitoring & maintenance
→ Building data culture across teams

This is the stage where the hats pile up :)

No data analyst should have to do it all ~ but most of us end up wearing a few hats anyway.

That's the job.

And honestly? Every messy dataset, every "urgent" dashboard request, every breakthrough insight makes it worth it.

Which stage resonates most with where you are right now?

Template - Vishakha :)

♻️ Share with someone navigating their data analytics journey

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Venkata Naga Sai Kumar Bysani

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The Battle

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Venkata Naga Sai Kumar Bysani

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