I help entry-level, non-technical professionals get their first data analyst role | Data Analyst @RBC | Data Career Coach

I help immigrants and career-switchers land analyst roles—without burning out on certificates.

By day I build decision-grade analytics (currently at Autodesk). By night I run Data Career Compass, where we treat your job search like a product launch:
Market → Positioning → Portfolio → Distribution → Interviews.

My POV:
Most candidates try to add more tools. Winners learn to package their value. Your portfolio isn’t a gallery; it’s a sales page for your judgment.

Receipts:

→ Professionals land interviews by aligning projects to business outcomes (not toy datasets).

→ My frameworks have been used across finance, tech, and education to translate experience into results.

→ Built data products and controls in regulated environments (RBC Capital Markets) that leaders rely on daily.


What you’ll get here:

→ Weekly breakdowns of real projects (SQL, Python, dbt, DuckDB, Looker/Tableau/Power BI).

→ Storytelling patterns that turn insights into action.

→ Templates that remove friction: portfolio briefs, stakeholder one-pagers, interview scorecards.

Coming April:

I’m launching a coaching program designed for mid-level and senior professionals ready to transition into analytics.

This isn’t an entry-level bootcamp—it’s a career accelerator that helps you reposition your existing experience, communicate business value, and step confidently into data-driven roles.

Work with me:

↳ Cohorts + 1:1 coaching → DM “HIRED”

↳ January program for mid/senior professionals → DM “APRIL” for early access

↳ Guest workshops for teams and communities → DM “WORKSHOP”


I build analysts who think like product managers and communicate like consultants.

Peter Ndiforchu

Every data analyst has lived through this meeting.

Dashboard says: $1.2M
Finance says: $1.1M

Meeting topic for the next 45 minutes:

“Where did the $100K go?”

First theory:
Maybe a filtering issue.

Second theory:
Currency conversion.

Third theory:
Time zone mismatch.

Fourth theory:
Someone forgot to refresh the data.

Meanwhile the analyst is quietly thinking:

“Please don’t be a duplicated join. Please don’t be a duplicated join. Please don’t be a duplicated join.”

45 minutes later…

Root cause:

Someone exported the numbers to Excel
…and manually deleted a row “that looked wrong.”

New meeting scheduled:

“Aligning reporting methodology across teams.”

Welcome to data analytics.

Where 90% of the job is not finding insights…

It’s explaining why two numbers that should match… don’t.

If you’ve ever spent an hour chasing a mysterious variance in a meeting, you’re officially part of the club. 📊😅

Helping Non-Technical Professionals Build Data-Driven Careers That Travel Globally | Data Career Strategist | Senior Data Analyst @ Autodesk

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

If you’re training for the old data science job, you’ll stay confused.

A lot of people are studying data science like it’s still 2016.

They think the job is:
“Build a model from scratch.”

But most real teams today need something different:

testing ideas fast

running clean experiments

measuring results

writing clearly

shipping work people can use

working with tools like Copilot and modern AI stacks

The modern data scientist is closer to a problem-solver than a “model builder.”

So if you’ve been feeling like:
“I’m learning, but I still don’t feel ready”

It might be because you’re preparing for a job that is not common anymore.

A better question to ask is:
“Can I take a business problem, use real-world data, and explain what I found?”

That skill travels across every domain.

P.S. A fancy model with no clear story does not get adopted.

✅ Comment “MODERN” and I’ll share a simple weekly routine to build these skills without burning out.
♻️ Repost for the person who thinks they have to master everything before applying.

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

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 

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.

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

Most people are learning data science the wrong way.

And no one wants to say it.

They are learning data science like it’s a subject.

It’s not.

It’s a job.

A lot of beginners spend months:

✓ Watching tutorials
✓ Completing exercises
✓ Memorizing model types
✓ Debating which algorithm is “better”

But they never practice this:

• Defining the problem
• Making trade-offs
• Explaining business impact
• Defending their choices

Data science is not about knowing everything.

It is about making decisions with incomplete information.

You do not become job-ready by finishing content.

You become job-ready by solving problems.

If your data science routine does not include:

Reading job descriptions
Building around repeated requirements
Writing about your reasoning

You are training for a classroom.

Not a career.

P.S. Be honest. Are you learning data science… or rehearsing data science?

✅ Save this for when you’re tempted to start another course.
♻️ Repost for someone rehearsing instead of building.

👋🏾 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.

The right way to learn data science

When I was applying to data science roles, companies kept seeing me as:
“High school math teacher.”

And they weren’t wrong.

That’s what my resume led with.

Twenty years of teaching.

Pages of experience.

My data science projects?

Buried.

So I made one change that shifted how I was seen.

I moved my projects up.

Not at the bottom.
Not after two decades of education.

At the top.

Where they had to see them.

Because recruiters don’t read resumes.

They scan.

And the first thing they see becomes the label.

If the top of your resume says “teacher,”
you’re a teacher.

If the top of your resume shows real data science work,
you become a data candidate.

Here’s the simple layout I recommend when you’re pivoting into data science:

• Short summary focused on the problems you solve
• Skills you can actually explain in an interview
• 2–3 projects with outcomes and tools
• Experience
• Education

Notice what moved up.

Projects.

Proof.

Because your resume has one job:

Get you interviews.

And in 2026, potential is not enough.

You have to show what you can do.

P.S. If you updated your resume today, would your data science projects be impossible to miss?

✅ Save this post so you remember to move your projects up.
♻️ Reshare for a career changer who keeps getting labeled by their past.

👋🏾 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.

No alternative text description for this image

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.

If your data science projects look perfect, I get nervous.

Perfect usually means guided.

Guided usually means copied.

Copied usually means untested.

Real data science work is rarely clean from start to finish.

It includes:

Incomplete data.
Shifting questions.
Constraints.
Time pressure.

When I review portfolios,
I am not looking for flawless output.

I am looking for thinking.

Why that method?
Why not another?
What changed mid-project?
What didn’t work?

That’s maturity.

That’s readiness.

Not polished notebooks with no explanation.

Data science is decision-making under uncertainty.

If your portfolio hides uncertainty,
it hides your growth.

P.S. If I removed your charts and only read your reasoning, would I trust your thinking?

✅ Save this for when you’re tempted to make everything look flawless.
♻️ Repost for someone afraid to show their process.

Data science projects shouldn’t be perfect

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.

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Tiffany Teasley

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taplio

The Battle

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Tiffany Teasley

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