🤖Data Scientist | Turning Raw Data into Money Generating Insights | Data-led Growth for Your Business 🚀

Freelance Data Scientist | ML Engineer | SQL | Python | Power BI | MLOps

Data analytics and Machine learning models built 5X faster - no quality compromise. Worked with 14 clients in 7 different industries in the last 3 years. Helped current company scale to $5M+ ARR. Featured TOP RATED PLUS talent on Upwork in 2025 with 100% job completion and satisfaction.

My Toolkit:
- Data analytics: Python, SQL, Excel
- ML Engineering: FastAPI, AWS, Streamlit, great_expectations
- Data visualization: PowerPoint, PowerBI, Plotly, Matplotlib
- Data modeling: Scikit-learn, TensorFlow, Keras
- MLOPs: CI/CD (GitHub Actions), Docker
- Additional tools: Notion, n8n, Canva, Git/GitHub

My Stats:
- 5-stars reviews on 99% of projects delivered.
- 100% Job Success Score for on-time, budget delivery.
- Helped scale currently business to $5M ARR with ATS analysis & Reporting.
- 1.5 years TOP RATER PLUS on Upwork.
- 1800 hours billed.
- $60k+ earned.

What You’ll Get Working With Me:
- A data analyst who is a real problem solver.
- Quick responses (within the hour on working hours)
- Clean, maintainable code and detailed documentation
- A clear “NO” if something isn’t feasible; no wasted time
- A well-crafted report or dashboard as per the requirements

My clients think:
- "Anas is great and highly knowledgeable. He is passionate about solving data science challenges and has helped me significantly streamline some of my processes, enabling better scalability.
- "Anas, was prompt and thorough and I would def use him again in the future."
- "Anas is great to work with. He made the job so fluid and his knowledge of data science makes him someone worth listening to. I’m glad I chose him for my small business. I’m looking forward to what’s coming next, and I would definitely hire him again."

Popular Clients & Products:
- Uhubs: Financial company based in London
- AllAboutGroup: Law recruitment firm based in London
- Growth Dynamics: Marketing and data strategist based in London.
- Adway: Tech and recruitment company based in Gotheborg, Sweden

About me:
- Football fan since I can remember (Real Madrid fan)
- Tennis lover and practitioner (one-handed backhand style)
- Mangas fan (One Piece, Naruto, Bleach)
- Travel addict (South Asia, Europe, Africa, America (soon))
- Reading is something I enjoy as much as coffee (oat latte fan recently)

-----

YouTube Channel
→ GO HERE: https://www.youtube.com/anas_riad

Request services via LinkedIn or Email below:
r.riadanas@gmail.com

Hire on Upwork:
https://www.upwork.com/freelancers/~0163377bf3029c7553

If you want to become a 10x data scientist

(in 2026), read these 10 papers:

1 Efficient BackProp 
↳ It’ll teach you practical tricks to train neural nets better and faster.

2 Random Forests 
↳ It’ll teach you how ensembles reduce overfitting and work well out-of-the-box.

3 A Few Useful Things to Know About Machine Learning 
↳ It’ll help you avoid common traps (data leakage, wrong metrics, bad assumptions).

4 XGBoost: A Scalable Tree Boosting System 
↳ It’ll teach you why boosted trees win so often, and how to run them fast at scale.

5 Adam: A Method for Stochastic Optimization 
↳ It’ll teach you the optimizer that made deep learning training much easier in practice.

6 Scikit-learn: Machine Learning in Python 
↳ It’ll teach you what “good ML tooling” looks like: clean APIs, pipelines, and baselines.

7 Spark: Cluster Computing with Working Sets 
↳ It’ll teach you how big data work happens in real teams: distributed compute + iterative ML.

8 Attention Is All You Need 
↳ It’ll teach you the Transformer, the base of modern NLP and most LLMs.

9 BERT: Pre-training of Deep Bidirectional Transformers 
↳ It’ll teach you why pretraining + fine-tuning changed NLP for good.

10 A Unified Approach to Interpreting Model Predictions (SHAP) 
↳ It’ll teach you how to explain model outputs in a way stakeholders can trust.

Learn from top researchers and builders by reading papers,
years of work in a few pages.

(What else should make this list?)

PS: links in comment below

♻️ Share with your network to benefit

undefined undefined

Data Scientist & BI Consultant @Adway • ML Engineer • YouTube @ Anas Riad

Not every problem needs an LLM. 
Some need 12 characters of regex.

I see people spending $𝟬.𝟬𝟯 𝗽𝗲𝗿 𝗔𝗣𝗜 𝗰𝗮𝗹𝗹 to extract emails from text.

Meanwhile: ⤵️

[𝗮-𝘇𝗔-𝗭𝟬-𝟵+_.-]+@[\𝘄-]+\.\𝘄+

Done. 𝗭𝗲𝗿𝗼 𝗰𝗼𝘀𝘁. Runs in milliseconds.

We're in 2026 and somehow we forgot that basic code still works. 

Not everything needs a prompt, a model, and a 3-second response time.

Need to validate a phone number? 
→ Regex. 

Need to clean whitespace from 10,000 rows? 
→ A one-liner. 

Need to rename 500 files? 
→ A bash script your laptop runs in 0.2 seconds.

LLMs are incredible for reasoning, writing, and ambiguity. 
But when the task has a clear rule and a predictable pattern, just write the rule.

Cheaper. Faster. More reliable.
Stop using a rocket ship to cross the street.

PS: Do you use LLMs for everything because it's convenient?

♻️ Repost if your network needs this reminder 
📌 Save it for the next time you almost open ChatGPT/Claude for string matching

Follow me for more → Anas Riad

Dear recruiters,

If you expect a Data Scientist to be an expert in:

→ Python for large-scale data analysis
→ Excel with complex financial-style reports
→ Pandas and NumPy on real, broken datasets
→ Heavy data cleaning and feature engineering
→ Git workflows, CI/CD, and automated testing
→ LLMs, prompt design, and applied AI use cases
→ Business KPIs, north-star metrics, and trade-offs
→ Clear storytelling with non-technical stakeholders
→ Dockerized environments and reproducible pipelines
→ Experiment tracking, model versioning, and rollbacks
→ Data pipelines touching cloud storage and warehouses
→ Regression, classification, and model selection at scale
→ Advanced SQL with messy joins and window functions
→ Exploratory analysis with Matplotlib, Plotly, and Seaborn
→ Probability theory, hypothesis testing, and A/B experiments
→ Bias–variance trade-offs and model performance monitoring

… you’re not hiring one 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁.
You’re describing an 𝗲𝗻𝘁𝗶𝗿𝗲 𝗱𝗮𝘁𝗮 𝗱𝗲𝗽𝗮𝗿𝘁𝗺𝗲𝗻𝘁.

PS: Post inspired by José Siles

📌 Follow Anas Riad for more content around data science

Anas Riad

YouTube is still the BEST place to learn ML.

Most people still watch the wrong videos.

Here are 𝟭𝟮 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝘃𝗶𝗱𝗲𝗼𝘀 𝘁𝗼 𝗺𝗮𝘀𝘁𝗲𝗿 ML ⤵️ 

---

𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝗖𝗦𝟮𝟮𝟵: 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
 
The legendary starting point for ML.
Covers core theory, intuition, and key algorithms.

Video: https://lnkd.in/e-aJRQun
 
---

𝗔𝗹𝗹 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 𝗶𝗻 𝟮𝟮 𝗠𝗶𝗻𝘂𝘁𝗲𝘀

A fast, structured overview of all major ML concepts.
Great for building a mental map before going deeper.
 
Video: https://lnkd.in/exCP58F6
 
---

𝗠𝗟𝗢𝗽𝘀 𝗖𝗼𝘂𝗿𝘀𝗲 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀
 
Shows how ML actually works in real products.
Pipelines, deployment, monitoring, and production mindset.
 
Video: https://lnkd.in/erzM3b4F

---

𝗛𝗼𝘄 𝘁𝗼 𝗗𝗲𝗽𝗹𝗼𝘆 𝗠𝗟 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 (𝗙𝗮𝘀𝘁𝗔𝗣𝗜, 𝗗𝗼𝗰𝗸𝗲𝗿, 𝗔𝗪𝗦)
 
From trained model to live service.
A practical deployment walkthrough with modern tools.
 
Video: https://lnkd.in/e3JtcAZk

---

𝗦𝘁𝗲𝗮𝗹 𝗠𝘆 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲

Learn how to structure ML projects properly.
Clean folders, reproducibility, and real-world practices.
 
Video: https://lnkd.in/eds2Buq8

---

𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗠𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻
 
A full ML project from start to finish.
Data prep, modeling, evaluation, and delivery.
 
Video: https://lnkd.in/ebx_dvtn

---

𝗠𝗟𝗢𝗽𝘀 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹 (𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗩𝗶𝗲𝘄𝗲𝗿 𝗦𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁)
 
Build a real ML pipeline using real data.
Feature engineering, modeling, and orchestration.
 
Video: https://lnkd.in/e9_xVb-Z

---

𝗬𝗼𝘂 𝗪𝗶𝗹𝗹 𝗡𝗢𝗧 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗶𝗻 𝟯 𝗠𝗼𝗻𝘁𝗵𝘀
 
A reality check on learning machine learning.
Focuses on process, depth, and consistency.
 
Video: https://lnkd.in/eRqYhJWK

---

𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗠𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁
 
A practical regression project walkthrough.
Covers evaluation, assumptions, trade-offs.
 
Video: https://lnkd.in/e2GEnb4N

---

𝗘𝘃𝗲𝗿𝘆 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱
 
A clear overview of common ML models.
 
Video: https://lnkd.in/eMW3YNKq

---

𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗚𝗿𝗮𝗱𝗲 𝗠𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹
 
Focus on building ML systems that last.
Code quality, deployment, and maintainability.
 
Video: https://lnkd.in/eXYhdxgt

---

𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀)
 
A simple introduction to ML definitions and concepts.
 
Video: https://lnkd.in/eT3qrGBt
 
---

📌 Save this for your next project.
♻️ Repost this to help your network in data.

90% of analysts use only 10% of Pandas.

Master these 10 operations. Level up instantly.

When I started learning data analysis,
I thought Pandas was “too big” to master.

Turns out… you only need the core.

Here are the operations I use almost daily:

• 𝗟𝗼𝗮𝗱 𝗱𝗮𝘁𝗮 → pd.read_csv()
• 𝗜𝗻𝘀𝗽𝗲𝗰𝘁 𝗾𝘂𝗶𝗰𝗸𝗹𝘆 → .head(), .info(), .describe()
• 𝗦𝗲𝗹𝗲𝗰𝘁 𝗰𝗼𝗹𝘂𝗺𝗻𝘀 → df[["col1","col2"]]
• 𝗙𝗶𝗹𝘁𝗲𝗿 𝗿𝗼𝘄𝘀 → df[df["col"] > value]
• 𝗚𝗿𝗼𝘂𝗽 & 𝗮𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 → .groupby().agg()
• 𝗖𝗿𝗲𝗮𝘁𝗲 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 → df["new"] = expression
• 𝗠𝗲𝗿𝗴𝗲 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 → pd.merge()
• 𝗛𝗮𝗻𝗱𝗹𝗲 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗲𝘀 → .isna(), .fillna()
• 𝗦𝗼𝗿𝘁 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 → .sort_values()
• 𝗘𝘅𝗽𝗼𝗿𝘁 𝗰𝗹𝗲𝗮𝗻 𝗱𝗮𝘁𝗮 → .to_csv() or .to_excel()

That’s it.

If you can combine these properly, you can solve 80% of real-world problems.

The rest is practice and business thinking.

PS: Is Pandas your go-to library for data analytics?

📌 Follow Anas Riad for more content around data science

The ML project you wish you built sooner.
2.5hr of hands-on learning ⤵️

No excuses for skipping end-to-end ML now! 🤣

(PS: Link to the full YT video in the comments)

When I first started working on churn prediction, 
I noticed something that always bothered me.

There are tons of Regression projects out there, 
but 𝟗𝟗% 𝐨𝐟 𝐭𝐡𝐞𝐦 stop at the notebook.

It’s like drawing the blueprint for a house but never building it.

So I created a full end-to-end regression project that goes from:
raw dataset → trained model → deployment → tests → cloud.

Here’s what I cover in this project & video:
→ Split data by date (time-series aware)
→ Data Quality with Great Expectations
→ Well-structured pipelines (feature, training & inference)
→ Unit & smoke tests to keep things reliable
→ Experiment tracking with MLflow
→ Containerization with Docker
→ FastAPI serving layer
→ CI/CD with GitHub Actions
→ AWS (ECS, ECR, S3, ALB) for Deployment

It’s the same type of stack I use in freelance + personal work, 
but applied to regression.

Looking forward to your thoughts on it.

🌟 Link to the full YT video in the comments

PS: I have another end-to-end coming soon, stay tuned 🤔

I started in data analytics in 2021. 
The job barely looks the same today.

Back then, your value was tied to tools.

You learned SQL. You learned Python. Maybe some Tableau or Power BI. And that technical gap between you and everyone else in the company? That was your edge. You could dig deeper than the person pulling numbers from a quick Excel sheet. That was enough.

Fast forward to 2025 and 2026.

AI writes code faster than most junior analysts. It reasons through data problems. It spots patterns. And yeah, I know what some of you are thinking, "but it makes mistakes." Sure. So do we. We miss things. We overlook patterns. We carry bias into every analysis without realising it. We're not the gold standard we like to think we are.

I've been working with AI daily for the past 3 years now. And what I've watched happen slowly at first, then all at once, is that the technical barrier that made analysts valuable is shrinking fast.

That directly hits entry level roles the hardest.

If your entire value is "I can write a SQL query and build a dashboard," you're competing with something that does it in seconds. Not in a hypothetical future. Right now.

But here's what I've also noticed in 3 years of doing this work:

The analysts who actually move the needle? They were never just "tool operators."

They're the ones who sit with stakeholders and ask the hard questions. They're the ones who take a messy data output and turn it into a recommendation that helps the business earn more, save more, or move faster. They understand context. They push back on bad assumptions. They connect the numbers to actual decisions.

No AI tool does that well yet.

So if you're early in your career or thinking about getting into analytics the path forward isn't about memorising SQL syntax or chasing every new tool.

It's about:

→ Going deeper than surface-level reporting  
→ Learning to frame problems before jumping into data  
→ Giving recommendations, not just dashboards  
→ Expanding into predictive models, time series, and forecasting  
→ Using AI as a multiplier, not pretending it doesn't exist

The role of data analyst isn't dying.

But the version of it that was just querying and reporting? That chapter is closing.

The analysts who evolve will be fine. Better than fine, actually, because AI gives you leverage that didn't exist 3 years ago.

The ones who don't evolve will wonder what happened.

♻️ Repost if you think someone in your network needs to hear this  
📌 Save for when you need the reminder yourself

Follow me → Anas Riad

Python vs SQL vs Excel.
Stop fighting over tools.

A few years ago, I treated these three like enemies.

Excel felt “too basic.”
SQL felt “too technical.”
Python felt “too complex.”

I thought I had to pick one and master it like it was a final boss.

Now?

I see them as tools.
Not competitors.

Each one solves a different problem.

Here’s how I actually use them:

🟢 Excel

→ Quick review of data
→ Fast ad-hoc analysis
→ Easy sharing with non-technical teammates

🟣 SQL

→ Querying relational databases
→ Filtering and aggregating structured data
→ Joining large tables efficiently

🔵 Python

→ Advanced analysis and automation
→ Machine learning workflows
→ Handling complex logic and larger datasets

The mistake isn’t choosing the “wrong” tool.
The mistake is not knowing when to use each.

Instead of asking:
“Which one is better?”

Ask:
“What does this problem require?”

Tools don’t compete. They complement.

PS: If you had to keep only one, which would it be and why?

(Repost for your network ♻️)

Most people get stuck between learning ML & deploying it.
Here’s the roadmap I wish I had ⤵️

PS: Link for the full YT video below ⭐

— — —

1️⃣ Python Programming
→ The foundation. Everything in ML starts here.
→ clean code, functions, data handling, and reproducibility.

🔗 Python Programming with Mosh (4hours): https://lnkd.in/e_y5APBb
🔗 100 Days of Code: https://lnkd.in/eHQn6HYk

— — —

2️⃣ Math & Statistics for ML
→ Build intuition for algorithms.
→ You can’t optimise what you don’t understand.

🔗 Statistics Fundamentals w/ StatsQuest (playlist): https://lnkd.in/ef3rg2UP
📕 Book: Practical Statistics for Data Scientists

— — —

3️⃣ Machine Learning Fundamentals
→ Learn how models think: data prep, feature engineering, model selection, and evaluation.

🔗 Stanford ML: https://lnkd.in/epviQVc8
🔗 ML specialization (coursera): https://lnkd.in/edbCT-3B
🔗 All ML fundamentals (short overview): https://lnkd.in/eSdDdMWS

— — —

4️⃣ Deep Learning Essentials
→ Understand how neural networks learn and train, from simple perceptrons to CNNs and Transformers.

🔗 MIT intro to deep learning (playlist): https://lnkd.in/ebS2_kvx
🔗 Understanding Deep Learning (notebooks): https://lnkd.in/eUyS-ggD

— — —

5️⃣ MLOps
→ The most in-demand skill today.
→ Automate, version, deploy, and monitor ML systems like a pro.

🔗 Docker for beginners: https://lnkd.in/eQXFqutQ
🔗 FastAPI for beginners: https://lnkd.in/eg7xnhsY
🔗 MLFlow tutorial: https://lnkd.in/enjdQ_zt
🔗 GitHub Actions tutorial: https://lnkd.in/etYr8DDf

— — —

6️⃣ End-to-End ML Projects
→ Bring it all together. Real data, real models, real deployment.
→ This is what turns knowledge into experience.

🔗 Get started with AWS: https://lnkd.in/e98PMn6d
🔗 ML Churn End-2-End (1.5 hours): https://lnkd.in/e3Fmdh3t
🔗 ML Regression End-2-End (2.5 hours): https://lnkd.in/ebMWGweb
📕 Book: Designing ML systems

— — —

Happy learning 😎 

🎥 Full video: https://lnkd.in/ekMz9MF8
💬 Comment below: which stage are you currently in?

I was never supposed to become a Data Scientist.

Nothing about my path pointed there.

I tried medicine.
Then architecture.
Spent 5 years on a Master’s in industrial engineering.
Worked as a professional DJ at the same time.

Then I found data science.
Late. Unexpectedly.
And honestly… it made no sense.

I changed countries.
I doubted myself constantly.
I had to spend one full year just learning how to speak better.
Five years ago, I couldn’t even express myself properly in this language.

Now I work in data science.
And create content in that same language.

It wasn’t linear.
It wasn’t clean.
And the odds were clearly against me.

What made the difference wasn’t just Python or maths.

Being a good data scientist is not only technical.

It’s also:

→ Being reliable when things break
→ Understanding the business, not just the model
→ Thinking clearly when data is messy
→ Explaining results so people actually act
→ Saying “this approach failed” and learning fast

Yes, you still need:

→ Coding
→ Statistics
→ Probability
→ Solid fundamentals

But that’s only part of the picture.

What really helped me:

→ Perseverance
→ Belief (especially when confidence was low)
→ Hard work
→ Learning from the right people

Data scientists don’t all look the same.
And they don’t come from the same background.

If your path feels messy, slow, or “wrong”,
you might actually be building something stronger.

PS: Did you find your path on the 1st try?

80% of analyst tasks will automate.

Will you still be relevant in 2026?

⭐ Venkata Naga Sai Kumar Bysani and I broke this down simply  ⤵️ 

1️⃣ Work with AI, not against it
Copilot, Claude, automation tools. Use them well. Guide them. Validate outputs.

2️⃣ SQL alone won’t cut it anymore
Add Python. Learn ML basics. Understand how data pipelines work.

3️⃣ Understand the business deeply
Dashboards are easy. Translating numbers into revenue, risk, and strategy is rare.

4️⃣ Solve problems, not tickets
No one cares about the tool. They care about impact.

5️⃣ Build systems that run without you
Automate reports. Schedule pipelines. Create alerts. Sleep while your work runs.

6️⃣ Explain insights clearly
If leadership can’t understand it, it won’t matter.

7️⃣ Share your work publicly
Document what you’re learning. Visibility creates leverage.

-- -- -- 

The analyst role isn’t disappearing.
It’s evolving.

And the ones who adapt will win big in 2026.

💬 What’s your take?
Is the Data Analyst role shrinking or just becoming more strategic?

📌 Follow Venkata Naga Sai Kumar Bysani & Anas Riad for top-tier data content
♻️ Repost this post with your network to benefit

50% cheaper LLM traffic.
98% quality retained.

Most teams I speak to are shipping AI fast.
But few are thinking about control.

Multiple models.
Different providers.
Rising token bills.
Hard-coded logic everywhere.

That’s where routing actually matters.

I’ve been exploring orq.ai and their standalone 𝗔𝗜 𝗥𝗼𝘂𝘁𝗲𝗿.

What stood out to me:
• One OpenAI-compatible endpoint for 20+ providers
• Smart routing: cheaper model for simple tasks, premium only when needed
• Tunable trade-off → ~50% cost reduction at ~98% quality
• Built-in fallbacks, retries, and guardrails
• EU-based, with data residency and compliance in mind

What I like most?

It decouples your app from the model.

No rewrites when pricing changes.
No panic when a provider throttles.
No glue-code routing logic to maintain.

For teams moving from prototype → production
control becomes more important than raw model power.

If you’re building multi-model systems,
It’s worth testing intelligent routing on your own traffic.

You can get an API key and try it in minutes.

📌 Start here: https://lnkd.in/ezA9CxC2

80% of reports are too long.
CEOs read less than 2 pages.

When I started freelancing, I made this mistake.

I once delivered a 25-page report to a CEO in the legal industry.

I was proud of it.
Deep analysis. Charts. Breakdowns. Everything.

He looked at it and said:
“𝗧𝗵𝗶𝘀 𝗰𝗼𝘂𝗹𝗱 𝗵𝗮𝘃𝗲 𝗯𝗲𝗲𝗻 𝘁𝘄𝗼 𝗽𝗮𝗴𝗲𝘀.”

That sentence changed how I report forever.

Here are 5 things your reports don’t need:

• Long documents No one reads 40 pages.
Clear beats comprehensive.

• Code snippets for non-technical audiences Executives want impact, not Python.
Translate insights into business language.

• Overdesign Too many colors and effects create noise.
Simple builds trust.

• No clear story Random charts don’t persuade anyone.
Every report needs a beginning, middle, and end.

• Trying to please everyone Reports are built for a specific audience.
One view does not fit all.

-- -- -- 

I learned the hard way:

Even if everything seems relevant, focus on the 20% that drives 80% of impact.

Since then, I write for the reader.
Not for my ego.

PS: do you fall for the 'long' report trap?

♻️ Repost for your network to benefit
📌 Follow Anas Riad for more content around data science

When platforms like ChatGPT become the interface between users and brands, visibility changes completely. It’s no longer just about ranking; it’s about being recommended by AI. It's as easy as letting Profound agents do the work.

The speed at which AEO has moved from “what is that?” to $1B valuation says everything. Big milestone for Profound.

Co-Founder at Profound

Profound has raised $96M at a $1B valuation from Lightspeed alongside Sequoia Capital, Kleiner Perkins, Evantic Capital, Saga Ventures and South Park Commons. 

In exactly 18 months, Profound has gone from an idea to serving 10% of the Fortune 500 and becoming the #1 leader on the G2 Grid for our category, and now unicorn status.

We’re building technology around two fundamental beliefs about where marketing is heading:

1/ Every company will care deeply about how AI talks about their brand.
2/ Every marketing team will use AI to do their job better and faster.

And, today we’re announcing:

• Profound Agents: Customizable AI workers that take marketing teams from campaign concept to execution, without cutting corners. 
• Profound University: Courses, certifications and cohort-based programs for marketers who want to lead in the AI era.

To every customer and partner who believed in this when it was just a thesis, thank you.

If you want to try Profound, comment “AGENT” below for free access to one of our most popular agents. No account required. No strings attached.

taplio

Anas Riad's Linkedin Analytics

Anas Riad

Performance ScoreAnas Riad's LinkedIn Performance

What is Anas talking about?

Who is engaging with Anas

Anas Riad's Best Posts (last 30 days)

Ready to grow your LinkedIn presence?

taplio

.css-6ytdur{background-image:linear-gradient(284deg, rgba(39, 43, 72, 1) 0%, rgba(33, 47, 183, 1) 100%);color:transparent;-webkit-background-clip:text;background-clip:text;}Anas Riad's Linkedin Analytics

Anas Riad

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);}Anas Riad's LinkedIn Performance

What is Anas talking about?

Who is engaging with Anas

Anas Riad's Best Posts .css-1emoo84{font-size:var(--chakra-fontSizes-md);font-weight:400;color:var(--chakra-colors-gray-600);}(last 30 days)

Ready to grow your LinkedIn presence?

Anas Riad's Linkedin Analytics

Performance ScoreAnas Riad's LinkedIn Performance

Anas Riad's Best Posts (last 30 days)