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 an Analysis Professional with a passion for turning requirements into actionable business insights. I have demonstrated my Capability in Data Analysis and Analyzing System requirements. Naturally passionate about analyzing detailed information, uncovering meaningful insights and making a transformative impact within the organization.

I consider myself a person who likes challenges, is resilient, and is adaptable. I also try to be efficient and add value to processes, by questioning their ways. Besides, I like to be engaged in projects with people from different backgrounds.

Specialties:
- Created visuals, process maps, and user guides for turning business requirements into functional features. 
- Demonstrated the ability to find efficient outcomes for the business, and stakeholders and prioritise deliverables.
- Produced User acceptance scenarios from Business Requirements to ensure that they align with business processes and requirements


Data Analytics Capability : 
-My proficiency in analytical thinking, SQL and Power BI has enabled me to effectively manage and analyze data. 
- Have demonstrated expertise in data analysis, Data Manipulation, and team collaboration. 
- Completed SQL projects focused on healthcare patient data and Spanish football data, showcasing my analytical abilities and proficiency in working with data.

With a passion for growth, I am committed to continuous learning and growth in the field of Analytics. I am eager to apply my skills and knowledge to tackle new challenges, contribute to different solutions, and make a positive impact on businesses.

taplio

The Battle

Tiffany Teasley

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The Battle

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Syed Mahiyan

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