Product Growth Analyst

Poshmark

Lead Product Analyst

➡️ Collaborate closely with Product Managers at Poshmark to improve Seller Experience & Live Show Launch & Features:
✔️ Ran 30+ Experiments on the Seller Experience side (A/B and Pre-post analysis)
✔️ 25% successful experiments that led to an increase of 3-4% of GMV
✔️ Led the implementation of mweb listings on the platform, increasing the number of listers by 18k/month, 0.3% of total listers.
✔️ Led Live Shows Launch and GTM effort working cross functionality with data eng and executives

Senior Product Analyst

Tophatter

➡️  Perform detailed analyses to distill key insights & drivers for retaining the customer base
✔️ Designed & ran more than 50+ experiments to measure the impact of new features to improve buyer and seller retention
✔️ Led, designed and built a set of solutions to address buyer retention, including usage of credits and personalized push notification that improve attendees retention by 25% and payer retention by 15%

➡️  Improve platform health with automation in User Acquisition and Supply 
✔️ ROAS optimization for multiple channels spend allocation resulting in a 50% increase in D14 and D28 ROAS
✔️ Improve Seller Funnel from Acquisition to Seller Retention that resulted in a 100% increase in retention and 35% in LTV (Spend per seller)

Growth Analyst

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

(still figuring out the rest)

You’re not the problem — the job market is.

I keep seeing “junior” roles asking for:
- 3+ years of SQL & Python 
- A/B testing knowledge
- Experience managing stakeholders

That’s not junior.
That’s companies filtering candidates.

They wanna pay less for more experience.
And because the market is benefiting them.

 They set the rules.
 And juniors - the least experienced - get stuck.

But here’s my recommendation:

If you’ve got strong SQL, good business sense, and can communicate insights clearly—you are enough.

Don’t let job descriptions fool you into thinking otherwise.

And if you cannot land that job, get unpaid volunteering experience, do projects for free, get that experience and then start applying.

Here are few resources to help you build these skills:
📕 The Data Book: https://lnkd.in/ei8Sp6Qw
📘 Data Roadmap: https://lnkd.in/eYpSaKMx

———

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Amney Mounir

Grow | LinkedIn

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Data Analytics & AI @ Meta

SQL is 𝘴𝘵𝘪𝘭𝘭 the king of data.

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

Here are few resources for you to get started:
📕 Interview Book: https://lnkd.in/ei8Sp6Qw
📘 Roadmap: https://lnkd.in/eSCDkmAU

———
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When I was trying to break into data,

I didn’t have mentors. I didn’t know what to study.
And honestly… I spent months feeling completely lost.

Doing projects helped me a lot to build my skillsets.

Here are in my opinion the best Github repos in 2025:

1) awesome-data-analysis

𝗟𝗶𝗻𝗸: https://lnkd.in/eVMPj3Nh
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻: A comprehensive list of SQL, Python, ML, and analytics resources — a perfect starting point for anyone building a full data foundation.

2) data-scientist-hanbook

𝗟𝗶𝗻𝗸: https://lnkd.in/eMzixkG3
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻: A curated, minimalist handbook from Andres that tells you exactly what to learn (and what to skip) for modern data roles.

3) cookiecutter-data-science

𝗟𝗶𝗻𝗸: https://lnkd.in/eBdvtNkE
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻: An industry-standard project template to structure your analytics and ML projects the same way professionals do.

4) data-science-template

𝗟𝗶𝗻𝗸: https://lnkd.in/erbER7XP
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻: A clean, reproducible project setup that teaches you how to build maintainable, production-ready data projects.

5) ML-From-Scratch

𝗟𝗶𝗻𝗸: https://lnkd.in/excyjzXn
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻: Clear, from-scratch implementations of key ML algorithms — perfect for understanding models and explaining them in interviews.

———

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What it feels to be a Data Analyst in 2025:

Saying “I’m learning ML” will NOT get you hired.

Showing 𝘪𝘮𝘱𝘢𝘤𝘵𝘧𝘶𝘭 projects you’ve built will.

Especially with code, models, and results.

Here are 5 projects to make your portfolio stand out as a Data Scientist or ML Engineer: 👇

1. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭 𝐂𝐡𝐮𝐫𝐧 𝐰𝐢𝐭𝐡 𝐗𝐆𝐁𝐨𝐨𝐬𝐭 

Use customer data to predict churn.
Clean data → feature engineering → tune your model.

Link: https://lnkd.in/eDcHXdy2

2. 𝐑𝐞𝐚𝐥 𝐀/𝐁 𝐓𝐞𝐬𝐭 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧

Run a simulated or real test.
Use t-tests or Bayesian stats to back up your insights.
Explain trade-offs clearly.

Link: https://lnkd.in/eZ2ZDr7z

3. 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 

Use Prophet or ARIMA to forecast revenue.
Clean the trend + seasonality. Plot actual vs. predicted.

Link: https://lnkd.in/ePFvMKkx

4. 𝐍𝐋𝐏 𝐒𝐞𝐧𝐭𝐢𝐦𝐞𝐧𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 

Scrape reviews. Clean and vectorize text.
Train a classifier (LogReg or BERT).

Link: https://lnkd.in/e4ncGEf5

5. 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐞𝐫 𝐒𝐲𝐬𝐭𝐞𝐦 (𝐀𝐢𝐫𝐛𝐧𝐛 𝐒𝐭𝐲𝐥𝐞)

Use collaborative filtering or content-based approach.
Matrix factorization, similarity scores, etc.

Link: https://lnkd.in/etiyDarf

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I analyzed 200+ Machine Learning job posts. 

And here is what I found:

Over 90% of ML interviews are 𝘤𝘰𝘯𝘤𝘦𝘱𝘵𝘶𝘢𝘭.
And don’t need you to code. 

The interview will assess how you think, explain, and reason through trade-offs.

Here are few questions you can expect:

1) How do you decide between Logistic and Linear Regression?

2) What’s the role of regularization in model performance?

3) How does Gradient Boosting differ from Random Forest?

4) Why does feature scaling matter for SVMs?

5) How do you evaluate models on imbalanced data?

If you have no clue how to answer these.

𝘐𝘵’𝘴 𝘧𝘪𝘯𝘦, 𝘥𝘰𝘯’𝘵 𝘸𝘰𝘳𝘳𝘺!

You can get better with 𝟑𝟔𝟓 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞

They are offering free 15-day access (Nov 6–21) to 115+ AI & Data Science courses and real-world projects.

📘 Access for FREE: https://bit.ly/4nHhCyA

———

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#AIandDataLearningChallenge

People will only show up when you get the job. 

Not when you’re stuck job hunting.

This process is hard, and you feel lonely, you're trying to stay motivated, trying not to take things personally, trying not to give up. 

Here’s what I wish someone told me:

- Rejection doesn’t mean you’re not good enough. 
- Your "yes" is coming soon
- Stay consistent

And I'm sure, 𝗬𝗢𝗨 will get there!

———

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Most aspiring Data Analysts stop at learning hard skills.

And that’s exactly why they get stuck.

They'll learn SQL, Python, Excel, Tableau...

But this is the part nobody talks about:

Being a great Data Analyst requires TWO skillsets.
And most people only build one.

Great Analysts can:

1) Solve open-ended, messy problems
2) Drive decisions from ideation → execution
3) Communicate insights people understand
4) Collaborate and build strong relationships

Don't get me wrong, I want you to master hard skills.
But invest twice as much in your soft skills!

That’s what will make you stand out in 2025.

———

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What differentiates Junior and Staff Data Analysts?

It’s mostly 3 things:

- Scope
- Influence
- Ownership

➡️ Junior Data Analyst:

- Works on small, well-defined problems
- Primarily executes tasks given to them
- Doesn’t influence decisions much

➡️ Senior Data Analyst:

- Owns a medium scope of work
- Have an opinion and influence discussions
- Shape direction rather than just following orders

➡️ Staff Data Analyst:

- Operates at a high scope, often across multiple teams
- Works as a consultant, delegates, and drives decisions
- Recognizes when things are off track and proactively corrects course

Where do you stand? 👇

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Acing your data interview takes more than technical skills.

You need to be able to:

1) Communicate clearly your projects
2) Being able to solve open-ended questions
3) Come up with the right metrics to measure
4) Be great at SQL (the most asked technical skill)

Being able to do these 4 things well, will put you in the best spot to land your next Data Role.

In the next year, I will expect AI being part of the interview process for Data Analysts.

Here are few resources to get you started:
📗 The Interview Book: https://lnkd.in/ei8Sp6Qw
📕 Interview Questions: https://lnkd.in/eHEb4u3P

———

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I’d have NEVER landed a job at Meta with 𝘫𝘶𝘴𝘵 SQL.

Here’s exactly what I needed to get there: 

1) Success Metrics

- Understand product goals
- Define your north Star
- Break it into input metrics
- Slide and dice them further

2) Investigation

- Clarify the drop
- Dig into internal factors
- Mention external factors
- Summarize all your findings

3) Product Ideation

- Clarify the goal
- Lay down the User Funnel
- Generate Ideas for each funnel step

4) Behavioral

- Use the S.T.A.R method
- Focus on metrics for your results & impact!

With all of that, you'll be safe out there.

You got this!

———

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Here is how I would prioritize my time to prepare for my next Data Analyst interview:

Junior (0-2 years)
- 70% SQL
- 20% Case Study
- 10% Behavioral

Mid-Level (2-4 years)
- 50% SQL
- 30% Case Study
- 20% Behavioral

Senior (5-8 years)
- 50% Case Study
- 30% Behavioral
- 20% SQL

Staff (8+ years)
- 50% Behavioral
- 40% Case Study
- 10% SQL

If you still don't know how to build your business sense, I highly recommend this free event from Leland, this will def set you apart if you start building this skill!

📕 Register here:  https://lnkd.in/eCgvyvnG

———

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This is all you need to know in Excel as a Data Analyst:

This is from my experience the things I've used in Google Sheets.

I never used Macros or other fancy stuffs.

If you are able to know these 7 things you're good to go:
- Pivot Tables
- Math Functions
- VLOOKUP
- Index Matching
- Charts
- Logical Functions
- HLOOKUP

Here are my favorite resource to get you ready:
📕 Data Book: https://lnkd.in/ei8Sp6Qw

———

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A Master’s Degree or PhD will 𝐍𝐎𝐓 save you anymore.

This is because the job market is changing faster than universities can adapt.

You don’t need more degrees.
You need 𝘯𝘦𝘸 skills built for this era.

Here’s what I’d focus on:

✅ Learn to compete with AI, not against it
✅ Sharpen your storytelling and business sense
✅ Start building — use AI tools like Replit or Lovable

I don’t know what the future of data jobs will look like.

But I know one thing — it won’t be the same.

So please, don’t hide behind degrees or certificates.
Keep adapting. Keep experimenting.

That’s how you stay relevant.

———

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A Master’s degree isn’t a golden ticket anymore.

“Do I really need one to get a data job?”
“Will it help me break into tech?”
“Is it worth all these debts?”

Before, having a Master’s 𝘸𝘢𝘴 an advantage.
But, today it's 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹.

You don’t need more degrees.

You need more skills:
- SQL
- Story-telling
- Product Sense
- Real Experience

That’s what gets you hired.
𝘕𝘖𝘛 a piece of paper that cost $60,000.

If you’re going to do it, do it for the right reasons:
→ immigration
→ networking
→ career break

But don’t do it because you think it 𝘨𝘶𝘢𝘳𝘢𝘯𝘵𝘦𝘦𝘴 a job.

Those days are gone.

———

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