Data Scientist | Consultant | Lean Six Sigma MBB

Most AI consultants begin with technology, hoping it will solve a problem.

I start with your business processes, identify the inefficiencies that are costing you money, and then apply data science and AI strategically to address them.

After working with companies from Fortune 50 giants to growing startups, I've learned that successful AI projects start with understanding the business problem, not chasing the latest technology.

My approach: Analyze your current processes, identify high-impact opportunities that actually move your revenue or costs, validate with quick prototypes, then build systems that work with your existing operations.

The difference: I don't just hand you a strategy deck. I build working demos in 48 hours to prove the concept works for your specific business before you commit significant resources.

Recent client results: Helped Teva Pharmaceuticals automate their marketing mix modeling system, saving countless manual hours. Built an end-to-end decision-making system that integrates multiple data sources for C-level strategic decisions. Improved a US e-commerce company's ROAS by 16% through advanced marketing analytics. Designed AI-powered document processing and business intelligence agents that streamline operations across multiple industries.

Bottom line: If you're hearing a lot about AI but are not sure how it applies to your business, let's start with your actual processes, not the technology hype.

🤝 Consultation Enquiries: https://data-science-ai.com/applynow/
 🐦 Twitter: https://twitter.com/p_rabadzhiyski

Plamen Rabadzhiyski

I love data science. But, my story is a bit different. 

I began as an Excel-Guru / Mechanical Engineer. Not a traditional background for a data scientist by any means. 

I used Excel until I reached its limits. 

I found R, and the rest is history:

- I began learning R, which led to applying data science to business 

- By applying data science, my team went from $3M/year to $15,000,000 per year

- I began consulting, which led to trainings all over the world

- I found my passion for teaching, and made a company out of it (Business Science!)

Since finding data science, I haven't worked a day in my life. It's been amazing. 

Let me know if you'd like to learn R, Python, or just want to chat data science.

Matt Dancho

This is what a data science team looks like in 2026. Not a team. A folder.

8 departments. 39 agents. Built in Python.

Every role is a markdown file.

Every department is a folder.

Every agent has memory, tools, and a job to do.

But here's what nobody is saying:

Agents don't own results. People do.

You still need one person who is accountable for what the agents produce.

That person is not a prompt engineer.

That person is a AI data scientist who knows how to build the system, ship it, monitor it, and tell the business what it means.

A year ago this required a team of 20.

Now it requires one Track 2 data scientist with the right portfolio.

The question is whether that person is you.

🚨 I'm running a free live workshop this week showing exactly how to build and ship AI / DS systems like this in Python.

Inside, you'll get:

• My Generative AI + Data Science Process
• Applied to a Business Problem (not "toy" data)
• Actual Python code + AI Agents

Used across teams tied to MRM McCann / S&P Global / Apple

👉Registration Link (500 seats): Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below

Generative AI, Data Science, Python, and Business (ROI). Join my next live AI workshop (free).👇

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80% of data scientists say they want to build AI agents.

Almost none of them can answer this question:

Which agentic pattern should you actually use?

There are 7. And picking the wrong one breaks your entire workflow.

Here's the quick breakdown:

1. Parallel — multiple agents run at the same time. Use when tasks are independent. Faster output.

2. Sequential — agents run one after another. Use when each step depends on the last. More reliable.

3. Loop — agent repeats until a condition is met. Use for evaluation, retry logic, quality checks.

4. Router — one agent decides which agent handles the task. Use when inputs vary widely.

5. Aggregator — multiple inputs feed into one agent. Use when you need a single synthesized output.

6. Network — agents talk to each other freely. Use for complex reasoning tasks.

7. Hierarchical — a planner agent coordinates worker agents. Use when you need to scale.

That last one — hierarchical — is what Cursor just used to write 1 million lines of code autonomously in one week.

It's also the pattern that separates Track 1 from Track 2 data scientists.

Track 1 knows what an AI agent is.

Track 2 knows which pattern to deploy — and can ship it in Python before the meeting ends.

🚨 If you’re worried about data science career death in 2026… 

I have a live workshop that will help you shift to Track 2 (AI/DS Systems Builder). 

Inside my live workshop, you'll get:

• My Generative AI + Data Science Process
• Applied to a Business Problem (not "toy" data)
• Actual Python code + AI Agents

👉Registration Link (500 seats): Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below

My 2026 AI data science stack:

1. Python ($0)
2. Pandas ($0)
3. Scikit Learn ($0)
4. LangChain ($0)
5. LangGraph ($0)
6. OpenAI API ($1/month)

You literally can grow into a $200,000 career for less than $12/year.

I’m teaching it live (free):
👉 Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below ￼

Harvard just open-sourced its entire ML Systems curriculum.
Free. Public. 6 pillars. Hundreds of pages.

And it won't get most data scientists any closer to a $150K AI role.

Here's why.

The book covers:

1. System Design
2. Data Engineering
3. Model Deployment
4. MLOps and Monitoring
5. Edge AI
6. Responsible AI

That's genuinely excellent material. Prof. Vijay Janapa Reddi built something worth bookmarking.

But here's what I've watched happen with 7,500 students over 8 years:

The ones who read everything and built nothing stayed stuck.

Lannon didn't get his $150K AI Engineer role because he read more.

He built a RAG Copilot in Python. Walked into the interview. Opened his laptop. Showed them the system running.

They hired him on the spot.

Heinrich didn't get Lead GenAI Engineer with a 20% raise because he studied deployment theory.

He went through 8 live clinics and walked in with a portfolio of working systems no other candidate had.

Knowledge is not the bottleneck.

Shipping is.

The gap between Track 1 and Track 2 data scientists is not what they know.

It's what they've built and can prove.

Bookmark the Harvard book. Read it. It's good.

And then ask yourself: what have I actually shipped this month?

🚨 If you want to go from reading about AI to building systems companies actually use —

I have a free live workshop this week.

Inside my live workshop, you'll get:

• My Generative AI + Data Science Process
• Applied to a Business Problem (not "toy" data)
• Actual Python code + AI Agents

Used across teams tied to MRM McCann / S&P Global / Apple

👉Registration Link (500 seats): Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below

In 2020, AI couldn't do your job for 30 seconds.

In 2026, it can do it for 7 hours straight.

You have one year before that number doubles. 

Here's how to prepare:

This isn't a prediction. There's a chart from Epoch AI that proves it.

From 2020 to 2024 — flatline. AI couldn't complete meaningful engineering or data tasks.

Then 2025 hits. The curve goes vertical.

And the tasks that crossed the line first?

- Train a classifier
- Fix bugs in Python libraries
- Run data analysis
- Build basic models

That's not "AI might take over someday."

That's your Tuesday.

Here's what makes this chart terrifying for Track 1 data scientists:

The curve doesn't plateau. It accelerates.

GPT-5 handles 3.5 hours of human work. GPT-5.2 handles 7. The next model won't stop at 14.

At some point — soon — the entire Track 1 data science workflow fits inside what AI completes before lunch.

Clean data. Build model. Write report. Done. Automated.

So the only question that matters is:

Are you building things AI replaces — or are you building the systems that deploy AI?

That's the Track 1 vs Track 2 split in one sentence.

Track 1 does the work AI is eating.

Track 2 builds the workflows that use AI as the engine.

Same Python skills. Completely different career trajectory.

On Wednesday, March 18th I'm showing exactly what Track 2 looks like — live, in Python, no prep needed.

Raw data → AI workflow → decision-ready output. 15 minutes.

The same pattern running inside teams at S&P Global, Apple, and MRM McCann.

Free. 500 seats. 2PM EST.

👉Registration Link (500 seats): Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below

RIP data analysis and modelling jobs.

Don't take my word for it.

Take Nature's.

"Data-analysis and modelling positions are already becoming obsolete."

Nature. February 2026.

150 years of scientific credibility just described what's happening to Track 1 data scientists right now.

Track 1: Clean data. Build model. Write report. Pray someone reads it.

Track 2: Ship AI workflow. Automate the decision. System runs without you.

One of these is what Nature is talking about.

One of these is getting paid $200K.

You already know which is which.

The uncomfortable truth is most data scientists built their entire career on Track 1 skills. EDA. sklearn. Jupyter. Handoffs.

Those skills aren't worthless. But they're no longer enough.

What companies are paying for now is someone who can take raw data and ship a workflow a team actually runs — not a notebook that lives on a drive nobody opens.

That's the gap. And it's widening.

On Wednesday March 18th I'm showing exactly how to close it.

Live Python demo. 15-minute workflow pattern. Raw data to decision-ready output.

The same pattern running inside teams at S&P Global, Apple, and MRM McCann.

Free. 500 seats. 2PM EST.

👉Registration Link: Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below

OpenAI, Google, and Anthropic just published guides on:

• Prompt engineering
• Building agents
• AI in business
• 601 AI use cases

9 of the best guides you can't miss:

1. AI in the Enterprise by OpenAI

Grab the PDF: https://lnkd.in/e4AcEV3S

2. A practical guide to building agents by OpenAI

Download here: https://lnkd.in/eMD-sMxp

3. Prompting Guide 101 by Google

Grab the PDF: https://lnkd.in/eMzuTs-h

4. Identifying and scaling AI use cases

Grab the PDF: https://lnkd.in/eHunEntp

5. Building Effective Agents by Anthropic

Grab the PDF: https://lnkd.in/eiSKayJ8

6. Prompt Engineering by Anthropic

Grab the PDF: https://lnkd.in/eCUmmcRj

7. Kaggle Agents Companion

Download here: https://lnkd.in/e_bVJbt4

8. Google: 601 real-world gen AI use cases from the world's leading organizations

Read here: https://lnkd.in/eJm_E4Rm

9. Prompt engineering by Google

Grab it here: https://lnkd.in/eDsvdXi8

===

🚨 What’s the next step (if you want to build an AI/DS portfolio for real)?

I have a live workshop that will help (free). Inside my live workshop, you'll get:

• My Generative AI + Data Science Process
• Applied to a Business Problem (not "toy" data)
• Actual Python code + AI Agents

👉Registration Link (500 seats): Comment “PORTFOLIO” and I'll share a registration link in the 1st comment below

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Plamen Rabadzhiyski

Matt Dancho

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The Battle

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