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 February:

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 “JANUARY” 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

Most candidates think they’re being evaluated on what they built.

They’re not.

When you explain a project, hiring managers are quietly assessing something much deeper.

Here’s what’s actually happening behind the scenes 👇

1️⃣ Can you think in business outcomes—or just tasks?
If your story starts with “I used SQL to…” and ends there, that’s a red flag.
They’re listening for why the project existed and what changed because of it.

2️⃣ Do you understand the problem—or just the solution?
Strong candidates explain the constraint, the tradeoff, and the decision.
Weak ones jump straight to the dashboard.

3️⃣ Can you simplify complexity?
If you can’t explain your project to a non-technical stakeholder in 60 seconds,
they assume you’ll struggle in real meetings.

4️⃣ Did you drive the work—or were you just assigned it?
Ownership shows up in language:

“I noticed…”

“I proposed…”

“I pushed back when…”


That’s leadership without the title.

5️⃣ Do you sound like someone they can put in front of executives?
Clarity. Structure. Calm confidence.
That’s what earns trust.

Here’s the uncomfortable truth:

Two candidates can build the same project.
Only one gets the offer—because they frame it differently.

Your projects don’t get you hired.
Your interpretation of them does.

If this resonated, next time you explain a project, ask yourself:
“Am I telling a story—or reciting a task list?”

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

What Data Hiring Managers Really Evaluate on Interviews

Most candidates know the STAR method.
Very few use it in a way that actually signals seniority in data roles.

Here’s how hiring managers experience it 👇

The STAR method for data roles (with a real example)

S — Situation
❌ Weak: “We didn’t have good dashboards.”
✅ Strong: “Sales leaders were making weekly decisions using spreadsheets pulled manually from three systems.”

Why this matters:
You’re showing business context, not just a messy data problem.

T — Task
❌ Weak: “I was asked to build a dashboard.”
✅ Strong: “I was responsible for creating a single source of truth that leadership could trust for forecasting.”

Why this matters:
This reframes you from executor to owner.

A — Action
❌ Weak: “I used SQL and Tableau.”
✅ Strong:
“I audited existing tables, wrote validation queries to reconcile numbers across sources, aligned definitions with Sales Ops, and designed a dashboard focused on decision-making—not vanity metrics.”

Why this matters:
Tools are expected. Judgment is not.

R — Result
❌ Weak: “They liked the dashboard.”
✅ Strong:
“Forecast variance dropped by 18%, manual reporting time was reduced by 6 hours per week, and the dashboard became the default reference in leadership meetings.”

Why this matters:
Impact beats effort. Every time.

The real mistake candidates make:
They treat STAR like a storytelling template.
Hiring managers hear it as a decision-making framework.

In data roles, STAR isn’t about what you built.
It’s about:

How you understood the business

How you made trade-offs

How your work changed behavior


If your STAR answers don’t reveal those signals,
you’re underselling yourself—no matter how strong your skills are.

📌 Save this before your next interview.

How I went from Cameroon → Canada → a US data role (without ‘local experience’).

When I moved, I assumed the biggest hurdle would be visas, credentials, or “local experience.”

It wasn’t.

The real question global recruiters were silently asking was:
“Who can vouch for this person when we can’t observe them directly?”

Here’s what made the difference on my journey 👇

1/ Relationships reduce hiring risk
Recruiters outside your country can’t see your work day-to-day. They rely on people they trust to validate your judgment and impact.
I focused on building genuine connections with:

Hiring managers

Senior data practitioners

People in the ecosystem who could confidently say,

 “I’ve worked with him. He delivers. I’d hire him again.”

2/ Visibility beats invisibility
I didn’t just rely on a resume. I made my work and thinking visible by:

Sharing real projects publicly

Discussing trade-offs and decisions

Engaging with communities in LinkedIn, Slack, and Discord

This gave recruiters context before even meeting me.

3/ Skills matter, but signals win
SQL, Python, Excel—all necessary. But what got me hired was showing:

Structured thinking

Clear communication of impact

Ability to navigate ambiguity

Coupled with endorsements from people who could vouch for me, geography became irrelevant.

Global hiring isn’t about ‘local experience’—it’s about trusted signals.

Ask yourself: “Who in this market can confidently vouch for my work?”

That’s the real game.

Cameroon → Canada → a US data role

The first time I realized hiring expectations weren’t universal…
I thought I had done everything right.

Strong technical skills.
Solid projects.
Clear results.

Then I walked into an interview outside my home market and something felt off.

They weren’t impressed by what tools I knew.
They kept probing how I worked.

Questions like:

“How did you influence stakeholders who didn’t agree with you?”

“What trade-offs did you make under pressure?”

“Who trusted your judgment and why?”


That’s when it clicked.

In some markets, hiring is about capability.
In others, it’s about credibility.
And in global roles, it’s about signal alignment.

Same resume.
Same experience.
Very different evaluation lens.

I stopped optimizing for: 
❌ tool checklists
❌ keyword stuffing
❌ “local experience” myths

And started optimizing for: 
✔ decision-making stories
✔ business judgment
✔ proof that someone would trust me in the room

That shift changed everything.

Global hiring isn’t harder.
It’s just calibrated differently.

If you’re preparing for roles beyond your home country, ask yourself: Are you proving skills…
or earning confidence?

Because globally, confidence hires before competence.

Here’s the uncomfortable truth most candidates don’t hear early enough:

Most data portfolios aren’t evaluated as “work.”
They’re evaluated as signals.

And most of them send the wrong ones.

I’ve reviewed a good number of portfolios as part of the hiring team.
The pattern is consistent:

What candidates show:
• Dashboards with no business question
• Kaggle-style analyses disconnected from real decisions
• Tools-heavy walkthroughs (“I used SQL, Python, Tableau…”)
• Perfect charts that solve imaginary problems

What hiring managers are actually scanning for:
• Can this person frame a messy business problem?
• Do they understand tradeoffs, not just techniques?
• Can they explain why something mattered — not just how they built it?
• Would I trust them to influence a decision with imperfect data?

Here’s the part most people miss:

A portfolio isn’t proof of skill.
It’s evidence of judgment.


That’s why two candidates can use the same dataset —
and only one gets a callback.

The one who: 
• Anchors the project to a real stakeholder decision
• Explains assumptions and constraints
• Shows what they didn’t do and why
• Communicates impact in plain business language

Strategic shift to make today:

Stop asking, “How do I show more tools?”
Start asking, “What decision does this help someone make?”

That question alone will put your portfolio ahead of 90% of applicants.

If you’re building a portfolio right now, build it like you already have the job — not like you’re trying to pass a course.

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

Applying to 100 data science jobs isn’t a strategy. It’s stress.
Here are 6 steps I would follow today:

1.  Pick three companies I genuinely care about.
2.  Study their data science job postings like cheat sheets.
3.  Build a project that mirrors the problems they’re hiring for.
4.  Reach out to someone on the data or ML team.
5.  Ask thoughtful, role-specific questions.
6.  Then apply, with a referral.

✨Focused applications beat mass applying every time.

P.S. Are you applying everywhere or being intentional?
#DataSistah

♻️ Repost if this helps someone rethink how they’re job searching.
📌 Save this if mass applying hasn’t been working.

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The Battle

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