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.

I love Data. And I love writing. I love writing about Data.
Open for brand collab.

(still figuring out the rest)

Amney Mounir

I went through this cycle too many times in my career.

1. You get excited about this new project.
2. Then you find out that the data is so bad.
3. You work your way to make some fixes.
4. You then like: "Hmm, it's not THAT bad".
5. You find some pattern & share them to the team.
6. And then nothing happens.

This is why YOU always need to 𝗺𝗮𝗿𝗸𝗲𝘁 your analysis.

You can do that by:
- Sharing your work more broadly (email or slack)
- Building relationship to have ppl spreading the word
- Having clear next steps/recommendations

So that all the work you do have: ACTUAL IMPACT.
And doesn't get thrown to the trash.

———
Btw Sai Kumar Bysani (200k+ followers) is hosting a FREE workshop on how to ACE your next data interview, that you should absolutely join!

📕 Data Workshop: https://lnkd.in/gbR_mCJW

———
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Most Data Analysts fail interviews for the wrong reasons.

Not because they don’t know SQL.
Not because they can’t build charts.
Not because they’re “not technical enough”.

But because 𝘁𝗵𝗲𝘆 𝗱𝗼𝗻’𝘁 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗴𝗮𝗺𝗲!

I put together this Data Analyst interview process
That breaks down every step:

1) What each round is really testing
2) How to prepare for each one
3) What interviewers actually care about

If you’re preparing for interviews in 2026,
𝘔𝘢𝘬𝘦 𝘴𝘶𝘳𝘦 𝘵𝘰 𝘳𝘦𝘱𝘰𝘴𝘵 𝘢𝘯𝘥 𝘴𝘢𝘷𝘦 𝘵𝘩𝘪𝘴 𝘱𝘰𝘴𝘵!

———

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When I worked as the 𝘰𝘯𝘭𝘺 Data Analyst, I hit a ceiling fast.

I was the "smartest" data person in the room, but that was actually a problem. I had no Lead Analyst to review my code. No mentor to challenge my assumptions. No one to help me level up.

So I started studying what other companies were doing.

Here are my top 5 technical case studies that leveled me up:

𝟭. 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗮 𝗣𝗶𝗰𝘁𝘂𝗿𝗲 𝗮𝘁 𝗡𝗲𝘁𝗳𝗹𝗶𝘅: 
https://lnkd.in/gvHY9w-n

𝟮. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗻𝗴 𝗮 𝗟𝗶𝘀𝘁𝗶𝗻𝗴'𝘀 𝗣𝗿𝗶𝗰𝗲 𝗮𝘁 𝗔𝗶𝗿𝗯𝗻𝗯: 
https://lnkd.in/gRQmyWw7

𝟯. 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗗𝗲𝗺𝗮𝗻𝗱 𝗮𝘁 𝗨𝗯𝗲𝗿: https://lnkd.in/gPegbSxY

𝟰. 𝗛𝗼𝘄 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 𝗟𝗲𝗮𝗱 𝗮𝘁 𝗠𝗲𝘁𝗮: https://lnkd.in/gFafCDi4

𝟱. 𝗨𝘀𝗶𝗻𝗴 𝗣𝗿𝗼𝗽𝗲𝗻𝘀𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 𝗮𝘁 𝗦𝗽𝗼𝘁𝗶𝗳𝘆: https://lnkd.in/g4wZZuHA

———

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Prompting will become a major skill for Data Analysts.

Not SQL.
Not Python.
Not building dashboard.

𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴.

I don’t believe AI will replace Data Analysts.
But Data Analysts with poor prompting skills will be left behind.
𝘣𝘺 𝘵𝘩𝘰𝘴𝘦 𝘸𝘩𝘰 𝘬𝘯𝘰𝘸 𝘩𝘰𝘸 𝘵𝘰 𝘶𝘴𝘦 𝘪𝘵 𝘸𝘦𝘭𝘭.

𝗜 𝗽𝘂𝘁 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿 𝘁𝗵𝗶𝘀 𝗔𝗜 𝗣𝗿𝗼𝗺𝗽𝘁 𝗰𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁 𝗳𝗼𝗿 𝘆𝗼𝘂.
Bad prompts vs Great prompts.
With Real work use cases.

AI will become a core skill in 2026.
The earlier you learn how to use it properly,
the bigger your edge will be.

———

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There is NO excuse in 2026 to say "I have no experience".

Here are 7 volunteering websites you can gain experience as a Data Analyst 👇:

1) VolunteerMatch
2) Catchafire
3) Techfleet 
4) Datakind
5) Statistics without Borders
6) United Nations Volunteers
7) Code for America

𝘠𝘦𝘴, 𝘪𝘵'𝘴 𝘶𝘯𝘱𝘢𝘪𝘥 𝘣𝘶𝘵...
Volunteering experience does 3 great things:

1) Gives you purpose while you wait
2) Keeps your skills sharp and relevant
3) Adds real experience to your resume

And that will get you closer to 𝘆𝗼𝘂𝗿 𝗳𝗶𝗿𝘀𝘁 𝘆𝗲𝘀!

———

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SQL is 𝘴𝘵𝘪𝘭𝘭 the king of data in 2026.

People keep saying “SQL is dead" but the reality is:

- Every interview still has a SQL round
- SQL is still the main tool to pull data
- SQL is still the most reliable language
- SQL isn’t a “nice-to-have", it's a must
- SQL is still good to know even with AI

Here are few resources for you to get started:
📗 Free Workshop: https://lnkd.in/gbR_mCJW
📕 Interview Book: https://lnkd.in/ei8Sp6Qw
📘 Roadmap: https://lnkd.in/gVaKx-tv

———
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Here is 𝘮𝘺 roadmap to master SQL in 14 days:

When I first started learning, I got stuck 
Because I didn't know where to start
What I needed was a clear path.

𝗗𝗮𝘆𝘀 𝟭–𝟰: 𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗦𝗤𝗟 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀

- Write your first queries: SELECT, FROM, WHERE
- Filter with LIKE, IN, BETWEEN, IS NULL
- Use ORDER BY, LIMIT, OFFSET
- Use Aggregate functions

𝗗𝗮𝘆𝘀 𝟱–𝟳: 𝗟𝗲𝗮𝗿𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗝𝗢𝗜𝗡𝗦

- Start by learning INNER and LEFT JOINs
- Then go for FULL OUTER, SELF JOINS
- As a Bonus, learn CROSS joins

𝗗𝗮𝘆𝘀 𝟴-𝟭𝟯: 𝗟𝗲𝘃𝗲𝗹 𝘂𝗽

- Use CASE statements
- CTEs and Subqueries
- Window Functions
- Text Analysis

Day 14 and beyond: Practice!

Here are some resources for you to get started:

📗 Data Workshop: https://lnkd.in/gbR_mCJW
📕 SQL Questions: https://lnkd.in/g62bfHF6

———
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Please stop taking 1000s courses if you’re still entry level. 

Companies that wanna hire you 𝘳𝘦𝘢𝘭𝘭𝘺 don't care. 

They care about: 𝘞𝘩𝘢𝘵 𝘺𝘰𝘶’𝘷𝘦 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘣𝘶𝘪𝘭𝘵.

Here are 5 projects to make your portfolio stand out 👇

1. 𝐄-𝐜𝐨𝐦𝐦𝐞𝐫𝐜𝐞 𝐅𝐮𝐧𝐧𝐞𝐥 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬

 Track how users move: view → cart → purchase.
 Spot drop-offs. Recommend fixes.

Link: https://lnkd.in/eDcHXdy2

2. 𝐀/𝐁 𝐓𝐞𝐬𝐭 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧
Run a real Experiment.
 Check if the change worked. Back it with stats.

Link: https://lnkd.in/eZ2ZDr7z

3. 𝐂𝐨𝐡𝐨𝐫𝐭 & 𝐑𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬
 See who sticks around.
 Plot retention by month or region.

Link: https://lnkd.in/ePFvMKkx

4. 𝐊𝐏𝐈 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝
Build a live dashboard: Sales, Profit, Users.
Add filters

Link: https://lnkd.in/e4ncGEf5

5. 𝐀𝐢𝐫𝐛𝐧𝐛 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐲
 Where should Airbnb expand?
 Analyze price, reviews, and trends.

Link: https://lnkd.in/etiyDarf

———

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Don't limit your job search to Data Analyst in 2026.

To increase your chances to land your next role, I highly recommend to expand your search to more Data Analyst roles.

There are so many, and here are a few for your reference:

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐑𝐨𝐥𝐞𝐬:
↳ Customer Insight Analyst
↳ Market Research Analyst
↳ Operation Analyst
↳ Business Analyst
↳ Strategy Analyst

𝐑𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐑𝐨𝐥𝐞𝐬:
↳ Analytics Associate
↳ Analytics Engineer
↳ Reporting Analyst
↳ BI Engineer
↳ BI Analyst

𝐏𝐫𝐨𝐝𝐮𝐜𝐭 & 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐑𝐨𝐥𝐞𝐬:
↳ Causal Inference Analyst
↳ Data Scientist, Analytics
↳ Experimentation Analyst
↳ Product Analyst
↳ Growth Analyst

𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 & 𝐌𝐋 𝐑𝐨𝐥𝐞𝐬:
↳ Machine Learning Engineer
↳ Forecasting Analyst
↳ Research Scientist
↳ Decision Scientist
↳ ML/AI Analyst

———

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Results don’t happen overnight, they take time.

Here is my Data career for the last 8 years:

- 2019: Junior Analyst in an e-commerce start-up
- 2020: Promoted to Product Analyst
- 2021: Joined a start-up as a Founding Analyst
- 2021 (3 months later): Laid off from that company
- 2021: Joined Poshmark as Sr. Product Analyst 
- 2023: Promoted to Lead Data Analyst
- 2024: Joined Meta as a Product Growth Analyst

And I don't even know what will happen in 2026.
And even beyond that.

But when I take a step back, I realize I've done a lot.

So be consistent in what you do, always take a step back to realize and enjoy all the great things you've accomplished.

And keep pushing for more! 

———

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The life of a Data Analyst looks like this every day.

“Can you pull this really quick?”
“Can you fix this dashboard?”
“Can you split it one more way?”

Early in my career, 𝗜 𝘀𝗮𝗶𝗱 𝘆𝗲𝘀 𝘁𝗼 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴.

What I didn’t realize:
Saying yes to everything often means spending time on work that doesn’t actually matter.

The best analysts aren’t the ones who answer more requests.

They’re the ones who push back and learn to say NO.

———

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Here are 4 things I'd master to become a Data Scientist:
𝘙𝘦𝘢𝘥 𝘶𝘯𝘵𝘪𝘭 𝘵𝘩𝘦 𝘦𝘯𝘥, 𝘴𝘰𝘮𝘦𝘵𝘩𝘪𝘯𝘨 𝘤𝘰𝘰𝘭 𝘵𝘰 𝘴𝘩𝘢𝘳𝘦...

𝟭/ 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗦𝗲𝗻𝘀𝗲

- Metric Definition (North Star vs.Guardrail Metrics)
- Diagnosing why a metric dropped 
- Trade-off (𝘸𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘭𝘢𝘶𝘯𝘤𝘩 𝘪𝘧 𝘹 𝘨𝘰𝘦𝘴 𝘶𝘱 𝘣𝘶𝘵 𝘺 𝘨𝘰𝘦𝘴 𝘥𝘰𝘸𝘯)

𝟮/ 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗦𝗸𝗶𝗹𝗹𝘀 

- SQL (Joins, Self-Joins, Window Functions)
- Pandas (aggregations, cleaning, merging)

𝟯/ 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 

- A/B Testing (Sample size, Power analysis)
- Causal Inferences (Propensity Score, Diff in diff)

𝟰/ 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 

- Using STAR Method
- Storytelling (explain results to any audience)

Btw, Banani, 𝗗𝗦 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗮𝘁 𝗪𝗮𝗹𝗺𝗮𝗿𝘁, 𝗶𝘀 𝗴𝗼𝗶𝗻𝗴 𝗹𝗶𝘃𝗲 𝘁𝗼 𝗰𝗼𝘃𝗲𝗿 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘁𝗼 𝗺𝗮𝘀𝘁𝗲𝗿 𝘆𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄. 

Make sure join, 𝘪𝘵'𝘴 𝘧𝘳𝘦𝘦 𝘢𝘯𝘥 100% 𝘸𝘰𝘳𝘵𝘩 𝘪𝘵.
📕 A/B Test Workshop: https://luma.com/t5xh23i3

———

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Most Data Analysts prepare for the wrong things in 2026.

So I tested something cool to learn about it:

I used 𝗖𝗼𝘂𝗻𝘁, an AI Agent to analyze 10k Data Analyst job postings from 2025 and 2026. 

Here’s what actually stood out:

𝟭) 𝗦𝗤𝗟 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝗞𝗜𝗡𝗚
It appears in 77.8% of Data Analyst roles.
(𝘠𝘦𝘴, 𝘴𝘵𝘪𝘭𝘭 𝘮𝘰𝘳𝘦 𝘪𝘮𝘱𝘰𝘳𝘵𝘢𝘯𝘵 𝘵𝘩𝘢𝘯 𝘗𝘺𝘵𝘩𝘰𝘯.)

𝟮) 𝗦𝗤𝗟 + 𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝘀𝗸𝗶𝗹𝗹 𝗰𝗼𝗺𝗯𝗼
Shows up in 50% of analyst jobs.

𝟯) 𝗪𝗵𝗮𝘁 𝗿𝗲𝗮𝗹𝗹𝘆 𝗺𝗮𝗸𝗲𝘀 𝘆𝗼𝘂 𝘀𝘁𝗮𝗻𝗱 𝗼𝘂𝘁:
- Analytical thinking (87.8%)
- Team work (82.9%)
- Business acumen (72.9%)

And the cool part is I was able to analyze all these jobs,
without writing a single line of SQL.

I just:
- Uploaded a CSV
- Asked Count AI agent my questions
- Got insights + visuals in few minutes

That's the kind of work that will take me few hours as a Data Analyst to pull, and by asking the right questions, I was able to save so much time!

𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘁𝗿𝘆 𝗶𝘁 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲, 𝗵𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗹𝗶𝗻𝗸:
https://lnkd.in/gtj3_u-K

—

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Insights to become a Data Analyst using an AI agent 

This is what your Data Analytics interview looks like:

𝗦𝘁𝗲𝗽 𝟭: 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿 𝗖𝗮𝗹𝗹 (𝟯𝟬 𝗺𝗶𝗻)
You’ll be asked about your experience, skills, and fit.

How to prepare:
- A 30-second pitch
- 3-4 relevant stories
- Mention skills like SQL, BI tools

𝗦𝘁𝗲𝗽 𝟮: 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗥𝗼𝘂𝗻𝗱 (𝟭𝗵)
Your SQL will be tested, often paired with product or business questions.

How to prepare:
- SQL (Basic functions, Joins, Window functions)
- How to improve a feature
- Investigate a metric drop
- Define success metrics

𝗙𝗶𝗻𝗮𝗹 𝗥𝗼𝘂𝗻𝗱 (𝟰-𝟱𝗵)

It's a deeper version of the technical round plus behavioral questions. You can expect to talk about a you're proud of, how you handle pressure and deal with multiple tasks. 

———

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I use less SQL at Meta than I did 6 month ago.

And I’m more 𝘪𝘮𝘱𝘢𝘤𝘵𝘧𝘶𝘭 than before.

Not because SQL doesn’t matter.
It does.

But because AI changed where my value shows up.

Today, I spend more time:
- Prompting instead of querying
- Framing the problem instead of ad-hocs
- Iterating on insights instead of debugging

AI help me handle the 𝘢𝘯𝘯𝘰𝘺𝘪𝘯𝘨 job, which is writing the SQL from scratch, instead I just take time reviewing and validating queries.

That freed up so much time time for thinking about strategic work which then drives so much impact for the team.

———

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Interviewers don’t tell you 𝙬𝙝𝙮 you failed.

And here is why:

If companies told candidates exactly how they were evaluated, interviews would stop working.

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝘄𝗼𝘂𝗹𝗱 𝗹𝗲𝗮𝗿𝗻 𝘁𝗵𝗲 𝗴𝗮𝗺𝗲.

And it would be much harder for them to separate strong candidates from bad ones.

So the process stays intentionally vague.

From my experience in tech, interviews usually come down to just a few things:

1) How you think through ambiguous problems
2) How clearly you communicate your thinking
3) How well you show impact from your past work

And every stage of the process has an internal scorecard, even if no one shows it to you.

Once you start preparing against that scorecard, interviews feel very different.

Much less random.
And MUCH more controllable.

———

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How much A/B testing should a Data Analyst know?

These are the 10 questions I usually get during interviews:

1. How would you set up your A/B test?
2. What parameters to calculate the sample size?
3. Which randomization unit ID would you use?
4. How long should we run this test? 
5. How do you account for network effects
6. What would you do if we cannot run an A/B test?
7. What is the purpose of running an A/A test?
8. Explain "p-value" to a non-technical audience?
9. Your success metric is positive (stat sig), but a guardrail metric was negative. What do you do?
10. What do you do if you can’t run an A/B test?

Banani Mohapatra, DS Lead at Walmart is hosting a free workshop to go through the A/B testing interview process in more details and common interviews questions. 

📕 Save your spot here: https://luma.com/t5xh23i3

Then thank me later.

———

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Universities have a major problem teaching Data Science.

They are full of 𝘱𝘳𝘰𝘧𝘦𝘴𝘴𝘰𝘳𝘴 with 0 experience in tech.

They teach you statistical theory. But they have no clue how to actually run an experiment at scale.

I wanna learn from those who work at the top.

Banani Mohapatra is one of them. She leads Data Science at Walmart, and she is doing a live workshop covering the real ML/DS interview process.

She is going to walk through:
- The end-to-end DS interview loop
- Real A/B testing interview cases
- And answer your questions

You shouldn't miss out. Really.

And it's completely free.

📕 Join here: https://luma.com/t5xh23i3

Enjoy. And thank me later.

taplio

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