Tiffany Teasley

I didn’t start my career in tech.

I spent twenty years teaching and transitioned into data science in my forties. I failed my first data science interview, fixed what I missed, turned it around, and got the job I originally interviewed for.

Today, I’m a working Data Scientist and AI Developer who’s been through the challenges of this path and now mentors and guides aspiring data scientists and career changers.

Credibility and recognition:
- LinkedIn Top Voice (2024-2025)
- Named one of the Amazing People at LexisNexis 
- "How I Got My First Job as a Data Scientist" Featured on Udemy and KDnuggets 

Through Data Sistah, I mentor aspiring data scientists through:
- Career coaching and clarity
- AmplifAI: guided, AI-enhanced portfolio development
- Resume, LinkedIn, and job search support

Learn more at: https://www.data-sistah.com/

Data Scientist

You should not be applying to data science jobs without a referral in 2026.
Let me explain.

I did not get hired through a referral.
But over the years, I’ve helped with recruitment where I work.

I’ve also spoken with recruiters, including some at FAANG companies.

They are overwhelmed.
Hundreds. 
Sometimes thousands of applications.

And with AI tools, the volume is even higher.

Most resumes look similar.
So how do you stand out?

Referrals.
A referral does not guarantee a job.

But it increases the chances your resume actually gets reviewed.
And that matters.

Here’s what most people get wrong:
They apply first.
Then try to network.
Do it the other way around.

Before you apply:
- Reach out to someone on the team
- Ask thoughtful questions
- Show real interest
- Have a conversation

Then if it makes sense, ask:
“Would you feel comfortable referring me?”

Now when you apply, your name is not random.
It is connected to someone inside.

That changes how your application is viewed.

This is not begging.
It is strategy.

When recruiters are flooded, you need leverage.
Referrals are leverage.

P.S. If you are mass applying without conversations, you are making it harder than it needs to be.
#DataSistah

📌 Save this if you are serious about landing interviews.
 ♻ Repost for someone applying blindly.

👋🏾 Hi, I’m Data Sistah.
I left a 20-year teaching career in my 40s to become a data scientist.
I share what I learned the hard way so you don’t have to.

Data Scientist | AI Developer | Founder, Data Sistah | Helping Aspiring Data Scientists Turn Skills Into Interviews

When I was trying to break into data science,
I thought finishing more courses would make me “ready.”

So I kept learning.
Building projects.
Working in private.

But being ready is not the same as being visible.
One thing I did not understand back then was this:

✨Open source projects count as experience.

And it is not just for experts.
You do not have to build something advanced.

You can:
↳ improve documentation
↳ add clearer examples
↳ clean up a README
↳ test features and report issues
↳ fix small bugs

That is contribution.

And contribution shows:
↳ you can read other people’s code
↳ you can communicate clearly
↳ you can collaborate
↳ you can work in a shared environment

That is real-world experience.

Because in a real job, you will not work alone.
You will review code.
You will explain your changes.
You will work on something that already exists.

Open source lets you practice that in public.

✨ If I had understood this earlier, I would have focused less on private notebooks and more on visible contribution.

Courses help you learn.
Contribution helps you grow.

P.S. Stop waiting to feel ready. Start contributing.
#DataSistah

📌 Save this if you are serious about building real experience.
 ♻ Repost for someone who thinks open source is only for experts.

👋🏾 Hi, I’m Data Sistah.
I left a 20-year teaching career in my 40s to become a data scientist.
I share what I learned the hard way so you don’t have to.

Open Source Counts as Data Science Experience

You don’t need more data science projects.


You need to understand the ones you already built.

After I failed my first data science interview because I didn’t know BERT, I didn’t go enroll in another big program.

I did 1 course. 2 guided projects.

But here’s what changed everything:

I made sure I could explain
↳ why I chose that approach
↳ what other options I considered
↳ what I would improve
↳ how it connects to business impact

That follow-up got me the job.

A notebook is not experience.
Being able to defend your decisions is.

Hiring managers don’t care if you followed steps.
They care if you can think.

Next time you finish a project, ask yourself:
Could I explain this clearly without looking at my code?

That’s the difference.

P.S. Are you building projects or building understanding?
#DataSistah

📌 Save this if you’re serious about getting hired.
 ♻ Repost for someone collecting projects but not reflecting on them.

👋🏾 Hi, I’m Data Sistah.
I left a 20-year teaching career in my 40s to become a data scientist.
I share what I learned the hard way so you don’t have to.

You don’t need a data science title
to start building data science experience.
You need direction.

If I had to start over today with zero experience, here’s exactly what I’d do over the next 30 days.

Week 1: Choose a lane.

Not five lanes.
Not “anything in data.”
One.
Analytics. Dashboards. NLP. LLM workflows.

Then I’d read 5 job descriptions in that space and look for patterns.

What skills keep showing up?
What tools are repeated?

That becomes the focus.

Week 2: Build one serious project.

Not three rushed ones.
One thoughtful one.
Use data that reflects a real problem.

And I’d document:
• What problem I’m solving
• Where the data came from
• Why I made certain decisions
• What didn’t work
• What I’d improve next time

That’s how you show thinking.

Week 3: Make it visible.

Write about what you built.
Explain your tradeoffs.
Share what confused you and how you worked through it.

Add it to LinkedIn.
Put it on your profile.
Turn it into a short case study.

Quiet work doesn’t get seen.

Week 4: Start conversations.

Reach out to a few professionals in that lane.
Not asking for a job.
Asking for perspective.

Share your project.
Ask what you could strengthen.
Ask what skills matter most where they work.

By the end of 30 days, you won’t just have watched tutorials.

You’ll have:
↳ A focused direction
↳ A real project
↳ Something public
↳ A few new connections

That’s experience.

When I was transitioning out of teaching in my 40s, I tried to learn everything at once.

It slowed me down.
Focus moves you forward.

P.S. Thirty focused days can shift your confidence more than six scattered months.
#DataSistah

📌 Save this if you need a clear plan.
♻ Repost for someone who feels stuck and needs structure.

👋🏾 Hi, I’m Data Sistah.
I left a 20-year teaching career in my 40s to become a data scientist.
I share what I learned the hard way so you don’t have to.

30 day plan to build real data science experience 

If you want data science experience, stop learning randomly.


In my first data science interview, they asked me about BERT.

I didn’t know it.
That moment showed me exactly what I was missing.

So I closed the gap.
After that interview, I completed the Udemy course:

“Learn BERT – the most powerful NLP algorithm by Google.”

Then I completed 2 Coursera guided projects:
→ Fine-Tune BERT for Text Classification with TensorFlow
→ Sentiment Analysis with Deep Learning using BERT

But finishing them was not the win.
The win was this:

I made sure I could explain
- Why BERT mattered.
- When to use it.
- How it compares to other models.
- What the tradeoffs were.

Then I followed up with the same hiring manager.
That follow-up changed everything.

Most people try to build experience by learning “everything.”

The faster way?
Find the gap.
Learn.
Close it.
Show your growth.

That’s how you build experience that actually counts.

P.S. If you just got rejected, don’t get discouraged. Study the gap.
#DataSistah

📌 Save this if you need direction.
 ♻ Repost for someone who thinks rejection is the end.

👋🏾 Hi, I’m Data Sistah.
I left a 20-year teaching career in my 40s to become a data scientist.
I share what I learned the hard way so you don’t have to.

I am a Founder by day and a Professor by night. My work revolves in the realm of NLP and Multi-Modal Systems. We created traversaal.ai with one vision: to provide scalable LLM Products for  Enterprises, which can seamlessly integrate within the existing ecosystem while being customizable and cost-efficient.

If you are building Gen AI capability within your infrastructure and finding it challenging, we might just have a solution for you that can help you go to market faster and tremendously reduce cost. 

For speaking, advisory, or collaboration inquiries, please send an email to: hello@traversaal.ai

Learn more about my courses on LLMs here: https://maven.com/boring-bot/

Background: I have 15+ years of experience of leading Data Science/ Machine Learning teams. My area of expertise is Experiment Design, NLP, Recommend-er Systems and Time Series forecasting.

My experience spans over three continent and seven countries across APAC, Middle East and Africa and North America and multiple industries, namely, Tech, Telecommunications, Finance and Retail.

Specialties: Large Language Models,Causal Inference, Deep Learning, Graphical Modeling, Cloud Computing, Retail Analytics,  Optimization, Game Theory, NLP

Hamza Farooq

🎯 Most AI conferences talk about the future. We’re building it.

I’m proud to serve as Conference Chair for Optimized AI Conference 2026, and this year’s lineup reflects exactly what we stand for:
"How are Enterprises using AI at Scale?"

📍 March 30–31, 2026 | Atlanta

While many conferences focus on demos and research papers, we focus on one thing:
Production AI. Deployed. Scaled. Working today inside Fortune 100 companies.
Not “what if.”
Not “someday.”
What’s delivering impact right now.

The Speaker Lineup Says It All
🔹 Netflix — Kamelia Aryafar, Head of AI for Members
🔹 Google — Paige Bailey (AI Developer Relations Lead) & Manisha Arora (Data Science Lead, Google Ads)
🔹 NVIDIA — Carter Abdallah (Dev Tech Engineer) & Vivek Kolasani (Solutions Architect)
🔹 AWS — Anton Alexander, Sr. Gen-AI Specialist
🔹 Apple — Ashok Prakash, Sr. ML Engineer
🔹 Walmart Global Tech — Jake Mannix, Technical Fellow

20+ more leaders actively shipping AI systems at scale.

What You’ll Walk Away With
✅ How Netflix personalizes experiences at scale
✅ Google’s approach to building Gemini
✅ NVIDIA’s frameworks for agentic architectures
✅ AWS patterns for enterprise GenAI deployment
✅ Real-world failure modes, and how to avoid them

This is battle-tested knowledge from teams running AI in production every day.

📍 March 30–31, 2026 | Atlanta
🎟️ Secure your spot: https://lnkd.in/d_4HTaue

I am launching a free three course series on Agent with Gabriela de Queiroz. Our focus is to debunk the hype and share what is really important. 

Thank you team Maven and Gagan Biyani for all your help and support!

CEO and Co-Founder at Maven. Previously Co-Founder at Udemy.

I never do listicles. But this feels like it deserves it. Here are 43 free lessons on AI Product Sense, Vibe Coding, and AI Engineering

Maven just soft-launched a new feature and it’s blowing up: multi-part free workshop series where each lesson builds on the next. Experts from Google Deepmind, Meta, AWS, Intercom, and more are now teaching free workshops series totaling 43 lessons…

AI Powered Design - 6 lessons hosted by Xinran Ma 
👉  https://lnkd.in/gTCcUXpF

AI Product Interviews - 4 lessons hosted by Mahesh Yadav 
👉  https://lnkd.in/gtG8tk8i

Vibe Coding - 9 lessons hosted by Harold D. 
👉  https://lnkd.in/gR3Mg678

AI Product Sense - 5 lessons hosted by Marily Nika, Ph.D 
👉  https://lnkd.in/gcx9M53A

AI Evals - 3 lessons hosted by Aishwarya Naresh Reganti 
👉  https://lnkd.in/gTM3ud58

Building an AI Team - 4 lessons hosted by Jacob Bank 
👉  https://lnkd.in/gwKnS-9X

Agentic AI - 3 lessons hosted by Hamza Farooq and Gabriela de Queiroz 
👉  https://lnkd.in/gz7m7kiA

UX Impact - 3 lessons hosted by Vitaly Friedman
👉  https://lnkd.in/gBhvNQJW

AI Engineering - 3 lessons hosted by Aurimas Griciūnas 
👉  https://lnkd.in/gjZcy4nj

AI Agent Mastery - 3 lessons hosted by Sara Davison and Tyler Fisk
👉  https://lnkd.in/gwGx__79

Last year we launched a free bootcamp and more than 10k users joined. This year, Gabriela de Queiroz and I are doing the same, for Product Managers!

The reason: 
Your AI agent works perfectly in the demo.
Then you ship it to users, and everything breaks.

That gap between demo and production? That's where most AI products fail.

Join us a three-part live workshop on shipping AI agents that don't fall apart in production.

Here's the problem most PMs face:
-You spend weeks refining prompts and workflows. Your agent finally works. --You demo it to stakeholders—everyone's impressed.

Then real users touch it, and:
-Edge cases you never imagined appear
-Reliability drops from 95% to 60%
-You have no metrics to diagnose what broke
-Every fix creates new unpredictable behavior

Sound familiar?

The real challenge isn't whether the agent works once. It's whether it behaves predictably at scale.

Drawing from our work with Fortune 100 companies and high-growth startups, we'll share the frameworks PMs need to: 
✅ Scope AI features properly 
✅ Evaluate them rigorously 
✅ Ship agents with confidence

What we'll cover:
Part 1: Vertical Automated AI Agents That Deliver Results Design enterprise-grade agents that retrieve, analyze, and collaborate to produce measurable outcomes.
Part 2: Building ReAct to Multi-Agent Architectures Evolve from single-agent systems to coordinated architectures that reason, plan, and execute reliably.
Part 3: Building Robust Evaluations for AI Agents Learn when an agent is genuinely working vs. just sounding confident. Design evaluations that catch failures before users do.
📅 February 24-26, 2026 ⏰ 12:00 PM PST 📍 Live on Zoom (recordings available)

🎟️ Register free →https://lnkd.in/d_bmTsRM

See you there.

Our amazing team: Jayita Chatterjee Amina Javaid Areej Mehboob

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The Battle

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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);}Tiffany Teasley's LinkedIn Performance

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