🤖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

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

Data Scientist & BI Consultant @Adway • ML Engineer • YouTube @ 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 ⤵️ 

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

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

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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

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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

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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

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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

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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

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

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

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

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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

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

Sambhav Chadha

Trusted to drive full-funnel growth for leading & fast-growing Health & Wellness brands.

Want to learn why?

DM me and I'll send across some case studies. 

p.s. fixed cost structure, no added fees regardless of how big we help you grow.

Director

🇺🇸 US + Natural Products Expo West stats.


Proper meals consumed during Expo West: 2
Snacks consumed during Expo West: 271 
Money lost to chess hustlers: $20
Professional cricketers met: 1
Hours in a single uber: 3.5
Nights of proper sleep: 0

ŌURA not particularly happy with me either.

Sambhav Chadha 

Building the #1 influencer partner for Health & Wellness Brands | Clients inc. Lululemon, Nestle, Huel, AG1, Fabletics, Fussy, Mother’s Earth, GIMBER, Turmeric Co. & more

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Everything I know about influencer attribution in 10 sentences:

1. Last-click attribution doesn't undercount influencer a little. It undercounts it a lot. Customers who discover a brand through a creator and buy three weeks later through a Google search look like a paid search win. They aren't.

2. The absence of a trackable click doesn't mean nothing happened. It means something happened that your current setup can't see.

3. The best proxy for real influencer impact is what happens to branded search volume, direct traffic, and organic conversion rate in the weeks after a campaign runs. These move. Most teams never check.

4. Creator codes and UTM links capture the customers who needed a nudge. They miss the ones who were convinced quietly - saw the post, thought about it, bought a week later through a different path entirely. Those buyers are often your best ones.

5. If your influencer program looks weak in attribution, the first question isn't "are the creators working?" It's "how are we measuring?"

6. Post-purchase surveys are the most underused tool in DTC. One question: "Where did you first hear about us?" The answer may surprise you.

7. Triangulating three imperfect signals gets you further than trusting one perfect-looking one. Tracked revenue, post-purchase survey responses, and branded search movement together tell a more honest story than any single attribution metric alone.

8. A measurement model that captures 80% of your influencer impact is still incredibly useful. Brands that wait for 100% accuracy before scaling are usually the ones who never scale.

9. Dark social is real. A creator posts. A viewer screenshots it, sends it to a friend on WhatsApp, and the friend buys the next morning. That sale is attributed to direct. You'll never know.

10. Influencer touches more of the funnel than most teams realise. It drives the first impression, feeds the retargeting pool, improves paid social CVR, lifts branded search, and accelerates organic. Attribution tools show you the last step. They rarely show you what started the journey.


Want to scale your brand with influencers? https://lnkd.in/e4N_Tw2j

I brought the smartest in-house influencer marketers in the world into a group chat (average brand doing ~$175M ARR).

Nobody had all the answers. But everyone had the same questions.


1. Measurement is the conversation nobody has solved.

Everyone's tracking impressions and EMV. But the real debate is what sits above that.

Sentiment keeps coming up - how do you actually measure it at scale without it being gamed by pods, trolls, and platform bias?

Nobody has a clean answer yet.

The consensus: no tool is perfect. The ones triangulating (MTA model, post-purchase surveys, and direct attribution together) are getting 60-70% of the way there.

The brands still waiting for a perfect attribution model are the ones not scaling.


2. The seeding → whitelisting play is working better than paid deals.

One member gifted a 40K creator. No brief. No contract. No paid fee.

The creator posted an unboxing on stories. The brand asked if they could whitelist it in exchange for two more weeks of product.

That content became one of the brand's top-performing Meta ads. £20K+ spend. No upfront cost.

The lesson the whole group took from it: authentic content with no brief often outperforms content you paid for. The brief is sometimes the problem.


3. Multi-category scaling is harder than brands admit.

Another brand asked how to split budget across two categories when one is clearly outperforming.

The answer the group landed on: 60-80% into your best-performing category, 20-40% into new categories to find your next 2-3 winners.

Make it explicit internally that the test budget is there to learn, not perform.

Niches get riches. Broader creator profiles dilute audience trust. Test categories separately first, blend only when both have signal.


The best part about this group isn't the answers.
It's that everyone in it is asking the same questions.

That's usually a sign you're working on the right problems.

12 months ago, I thought the only way to guarantee great client results was to do everything myself.

Control freak? Maybe.

But here's what that version of me didn't understand: the ceiling on what we could build was essentially my own capacity.

And my capacity has a pretty hard limit.

The shift happened gradually and then all at once.

We started hiring people who cared about the work the way I did.

Not just people who could execute.

But people who'd send a creator a personal note because it felt right, not because it was in the brief. People who'd lose sleep over a campaign underperforming the same way I would.

That's harder to find than it sounds.
Most people clock in and clock out.

The team we've built genuinely doesn't.

And the results speak for themselves.

In January, influencers posted about our clients 6,500 times. One post every 7 minutes - around the clock. Not one of those creators was paid a fee.

That's the output of a team that shows up every single day and does the unsexy work at an exceptionally high standard.

And it's why our brands regularly go out of their way to compliment our team.

I'm proud of the results we produce.
But I'm prouder of the people producing them.

If you're building something in health & wellness and want to see more of my team's work, DM me and I'll send over some case studies.

Most influencer and affiliate programs don’t fail because of creators.

They fail because there’s no engine behind them.

Here’s what we consistently see:

• Affiliate treated as an add-on instead of the core incentive layer
• No system for generating consistent creative volume
• Winning content not licensed or amplified
• Data not fed back into sourcing or briefs

So growth plateaus.

On March 12th, at the Influencer Marketing Summit I’ll be unpacking how to fix that.

We call it the DRIVE framework:

Discovery
Relationships
Iteration
Viral Amplification
Evidence-Based Optimisation

It’s the operating system behind brands that don’t just “run influencer” - they compound it.

If you’re responsible for growth and influencer still feels messy or unpredictable, this session will give you a blueprint.

🗓 When: March 12
💻 Where: Virtual
🎟 Free registration 

Save your seat via the link below

The question on my mind for Augmentum Media right now is:

• "How do we grow up, without growing old?"

You may get the irony in that.

We're a young agency. We started in a student flat. We've built fast, shipped fast, and figured things out in public.

That energy got us here.

But I don't think it's what gets us to where we're going.

So I'm currently asking myself a harder question: how do we mature, without feeling outdated? 

My current hypothesis:

1. Creating more moments for people to connect.

Events, rooms, conversations - things that exist in the real world and pull people in. Sounds fluffy, but creating more moments is my #1 focus this year.

2. Leading more with brand and less with founders.

This one is uncomfortable to say out loud. Founder-led content works. It's working for us right now. But a business that depends on two people being visible all the time isn't a brand - it's a personal brand with a team attached. We want to change that.

3. Raising the quality bar.

We've shipped a lot of imperfect things fast and learned from them. There's a version of that which is startup energy. There's another version which is just sloppiness dressed up as iteration. I think we've been too comfortable with the latter.

I could be completely wrong about all of this.

Maybe the founder-led approach is the moat, not the bottleneck.

Maybe "moments" is a distraction when the product is still the priority.

Maybe speed and imperfection is still the right bet at our stage.

I genuinely want to hear other perspectives on this - from people who've navigated it, got it wrong, or think my logic is flawed.

What does "growing up, without growing old" look like to you?

taplio

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taplio

The Battle

Anas Riad

Sambhav Chadha

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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);}Anas Riad's LinkedIn Performance

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