Product Growth Analyst

Dataford

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Founder

Poshmark

Lead Data 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

Cultivate

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

Experienced Lead Product Analyst with over 6+ years of experience.

I founded Dataford.io, to help people in their journey to break into Data roles. 

I have a proven track record of successful experimentation and data/business analysis. Improved business metrics at Poshmark and Tophatter, including GMV, lister growth, retention, and ROAS. Led over 100+ experiments and improvements resulting in significant increases in key business metrics.

Let's connect!

I don't care who you are: learn SQL.

And here is a cheat sheet to get you started. 

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Amney Mounir

Product Growth at Meta | Mentor at Dataford.io

Soft skills beat technical skills in Data Analytics, all day long...

Many people believe that writing great SQL or Python will make them a better Data Analyst.

OMG! No, no, noooo!

This is NOT true.

As a Data Analyst, you need to drive business value to your company.

And you don't need fancy models or be the best dashboard builder.

YOU need to focus on better understanding business problems, be good at explaining them to stakeholders and find actionable solutions to them!

I've met great technical people in my career that were like:

"In January, Revenue dropped 3% Year over year."

How am I supposed to do with this? 

The best ones I met said things like:

"We are noticing that customer churn increased by 5%, after the release of this new sign-up flow. Given Revenue drop, is mostly coming from new users, this is more likely due to this change. We should test again with the old flow and check the revenue impact. If this is the main reason, I would advise to rollback."

Be better than SQL or Python.

Because at the end of the day, company don't care if you good in technical skills, if you cannot provide them REAL value.

This post will not go viral.
But who cares? If 1 person sees value to it, I'm happy!

♻ Repost if you find this post relevant

You'll never land a job in Data.



I remember hearing that voice. Don't listen to it.

𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝗺𝘆 𝟱 𝘁𝗶𝗽𝘀 𝘁𝗼 𝗴𝗲𝘁 𝘆𝗼𝘂 𝘁𝗵𝗲𝗿𝗲:

𝟭) 𝗪𝗿𝗶𝘁𝗲 𝗖𝗼𝗱𝗲 𝗘𝘃𝗲𝗿𝘆 𝗗𝗮𝘆

- Solve 2 to 3 Real SQL Interview Questions daily on Dataford
- Join Real Challenges on Kaggle and contribute to the community
- Extend your skills with Python for Data Analytics and Statistics

𝟮) 𝗙𝗮𝗸𝗲 𝗶𝘁 𝘂𝗻𝘁𝗶𝗹 𝘆𝗼𝘂 𝗺𝗮𝗸𝗲 𝗶𝘁

- Include data analysis into your current role, even if you're not officially one yet.
- Offer to take on your team’s reporting tasks or support any of their projects.
- Change your resume job title to match the position you want.

𝟯) 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗩𝗘𝗥𝗬 𝘀𝘁𝗿𝗼𝗻𝗴 𝗻𝗲𝘁𝘄𝗼𝗿𝗸

- Follow Data Analyst/Scientist and publications like Towards Data Science and Hugging Face.
- Connect with Data Analyst/Scientists on LinkedIn (mostly the ones that work in companies you aim to join at some point!)
- Every expert started as a beginner, so 𝗱𝗼𝗻'𝘁 𝗯𝗲 𝗮𝗳𝗿𝗮𝗶𝗱 𝘁𝗼 𝗿𝗲𝗮𝗰𝗵 𝗼𝘂𝘁.

𝟰) 𝗚𝗮𝗶𝗻 𝘀𝗼𝗺𝗲 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲

- Apply for internships, even unpaid ones, to gain practical experience.
- Reach out to small businesses or startups through community groups and offer your skills for free
 - 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲 𝗴𝗮𝗶𝗻𝗶𝗻𝗴 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗼𝘃𝗲𝗿 𝗺𝗮𝗸𝗶𝗻𝗴 𝗺𝗼𝗻𝗲𝘆 𝗮𝘁 𝘁𝗵𝗶𝘀 𝘀𝘁𝗮𝗴𝗲.

𝟱) 𝗘𝗻𝗷𝗼𝘆 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀

- Think of landing a job as a game where you pass levels and have fun along the way.
- Break down your job search into funnel steps:

Resume --> Recruiter Interview --> Hiring Manager Round --> Last Round

And get better on each step!
 
Don't forget to 𝗳𝗼𝗰𝘂𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗵𝗲 𝗲𝗻𝗱 𝗴𝗼𝗮𝗹

𝟲) 𝗥𝗲𝗽𝗼𝘀𝘁 𝗮𝗻𝗱 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲

If you don't, you will probably never land a job 😂 (jk!)

5 Proven Tips to land your first data job

Things have changed.

♻️ Repost if you think it’s true 😆

No alternative text description for this image

Joins are the most used commands in SQL. Here is what you need to know:

↳ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗝𝗼𝗶𝗻?

Imagine you have two datasets: one with customer details and another with their orders. To find out which customers (by ID and first name) placed orders, you need both sets together.

A join creates a temporary table merging these datasets based on a common field.

In this case, the customer_id in the customer table matches the customer_id in the orders table. Joining them on this field combines the relevant info from both.

↳ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮𝗻 𝗢𝘂𝘁𝗲𝗿 𝗝𝗼𝗶𝗻?

But what if you want to see all the customers regardless of they ordered or not?

This is where you will use an outer join.

You can use a LEFT JOIN starting from the customers table to the orders table.

This will get you all the customers including the ones without any orders.

↳ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗖𝗿𝗼𝘀𝘀 𝗝𝗼𝗶𝗻?

Cross join combines every record from the first table with every record from the second table.

↳ 𝗦𝗲𝗹𝗳 𝗝𝗼𝗶𝗻𝘀

Self joins are used to join a table to itself, helping to compare rows within the same table.

For example, if you have a customer table and want to find customers who live in the same city as each other, a self join on the city field can help you for that!

♻️ Repost this if you found it useful!

😇 Follow me for more content about Data Analytics

All you need to know about SQL Joins

Can ChatGPT write better SQL queries than a Data Analyst? Let's find out!

The game will be based on 3 SQL questions from Dataford:

1 easy: Top Drivers from Uber
1 medium: High-Frequency Customers from Doordash
1 hard: Consecutive Login Days from Tiktok

You can check the details of each answer on the carousel, but here are the results:

Question 1 (Easy):
✅ Me
✅ ChatGPT (and faster!)

Question 2 (Medium):
✅   Me
✅  ChatGPT (with a slower run time 🥱)

Question 3 (Hard):
✅ Me
❌ ChatGPT (with 2 hints, was still not able to get it right!)

I think it’s fair to say that we (as Data Analysts) “won” this challenge against ChatGPT. 

What I can is: ChatGPT is definitely faster than us at writing queries - it needs seconds vs. minutes for us. 

But it still got 33% of its answers wrong. 

And even if it did complete all of it right, it won't replace your job.

Think of ChatGPT as a tool to help assist you and make you more efficient.

You still need to collect, organize, and share the insights with the team, and that part I don't believe will be replaced anytime soon.

♻️ Repost if you agree with this (even if not 😂)

ChatGPT vs Analysts - Who wins?

Want to land more Data Science and Data Analytics interviews? 

Then start with improving your resume.

And here is my checklist:

✅ Your summary section:

- This is your sales pitch!
- Make it relevant to the role you’re applying for!
- Keep it up to 3 lines and add the right keywords

✅ Your Relevant Experience

- Describe your responsibilities and achievements!
- Use verbs such as Led, Improved, Drove
- Be specific and metrics-oriented 
- It's time to "fake it until you make it" time with great achievements

✅ Your Education

- At the end, add your education
- No need to add any year to avoid age bias

With that, you should be good to go and start applying!

But wait...

I am offering you my Resume template for FREE.

To access it, go to Dataford website under Workspace

---------------------------------------
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Here are the 3 advice I would give to someone starting to learn SQL:


✅ 𝗝𝘂𝘀𝘁 𝗦𝘁𝗮𝗿𝘁: 

Don’t stress over finding the perfect course.

Pick one that seems interesting and dive in.

Most beginner courses cover similar foundational topics.

The key is to start.

✅ 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗗𝗮𝗶𝗹𝘆: 

Consistent practice is key. 

Solve exercise problems regularly to grasp SQL concepts. 

The more you practice, the better you get.

✅ 𝗕𝗲 𝗣𝗮𝘁𝗶𝗲𝗻𝘁: 

Mastering SQL won’t happen overnight. 

Give yourself time to absorb the material. 

You can learn enough in just two weeks to secure an entry-level position. 

That’s exactly what I did, you simply need to start!

——

♻️ Repost this if you found this useful.

👋 And follow me for more data tips like this

𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝘄𝗶𝗹𝗹 𝗺𝗮𝗸𝗲 𝘆𝗼𝘂 𝗮 𝗚𝗥𝗘𝗔𝗧 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁:


𝟭. 𝗕𝗲 𝗙𝗹𝘂𝗲𝗻𝘁 𝗶𝗻 𝗦𝗤𝗟

Great Data Analysts can write SQL as if they were writing English—without thinking much.

Practice daily on Dataford or any other platforms to keep your skills sharp.

If you’re working in a company, review your colleagues’ code and learn from it.

𝟮. 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗦𝘁𝗮𝘁𝘀

People often underestimate this.

Great Data Analysts have a solid understanding of descriptive stats.

They know the difference between a sample and a population and how that relates to statistical testing.

They also understand how A/B testing works, including what a p-value and power are.

My favorite book to learn is: Trustworthy Online Controlled Experiments, built by experimentation leaders at Google, LinkedIn, and Microsoft.

𝟯. 𝗗𝗲𝗲𝗽 𝗗𝗼𝗺𝗮𝗶𝗻 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲

Great Data Analysts know their stuff:

- They learn from their colleagues.
- They read industry reports.
- They follow creators and experts in their niche.

𝟰. 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲

The best data Analysts who always exceed expectations are the ones who say NO a lot.

They know what matters and are always looking to make IMPACT.

Don't say YES to everything, you will end up overwhelmed, and eventually fail at being a GREAT analyst for your company.

Instead, work on projects with high visibility and impact and you’ll see great results!

𝟱. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗦𝗲𝗻𝘀𝗲

Great Data Analysts communicate effectively and understand which insights are valuable.

This is what you can do to improve yours:

- Read books; my favorites are Crack the PM Interview, Case in Point, and Inspired.
- Watch Real Mock Interviews from Experts on Dataford’s Interview Hub.


With these 5 pillars, you’ll be well on your way to becoming a GREAT Data Analyst.


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👋 I post daily tips about Data Analytics. Follow me for more!

Here's how I landed offers from Meta, DoorDash, and other top tech companies:

𝗦𝘁𝗲𝗽 𝟭: 𝗣𝗵𝗼𝗻𝗲 𝗰𝗮𝗹𝗹 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗿𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿

The interview lasts about 30 minutes. The recruiter will ask about your experience to ensure you fit the role well.

𝗠𝘆 𝘁𝗶𝗽 💡: Prepare a 30-second pitch about yourself, highlighting your relevant experience and why you want to join the company. Be sure to mention all relevant skills, such as SQL, Python, and A/B testing.

𝗦𝘁𝗲𝗽 𝟮: 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄

The next round is the technical interview, where they'll test your SQL knowledge. Make sure you’ve mastered this language!

𝗠𝘆 𝘁𝗶𝗽 💡: Sign up on the Dataford website and start practicing across all these 6 topics

→ Basic Functions
→ Ratios
→ Ranking
→ Window Functions
→ Percentile
→ Text Analysis

𝗦𝘁𝗲𝗽 𝟯: 𝗧𝗮𝗸𝗲-𝗛𝗼𝗺𝗲 𝗔𝘀𝘀𝗶𝗴𝗻𝗺𝗲𝗻𝘁 (𝘀𝗼𝗺𝗲𝘁𝗶𝗺𝗲𝘀!)

I really hate when companies ask for this. 

𝗠𝘆 𝘁𝗶𝗽 💡: If they do, make sure they pay you for your time—your time is super valuable.

𝗦𝘁𝗲𝗽 𝟰: 𝗧𝗵𝗲 𝗢𝗻-𝘀𝗶𝘁𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄

You will get to meet 4-5 people, ~45 min each:

→ 1 SQL Interview
→ 2-3 problem solving (i.e case studies) 
→ 1 behavioral round

𝗠𝘆 𝘁𝗶𝗽 💡: 

→ Practice business sense frameworks because they'll ask open-ended questions to assess your problem-solving skills. 
→ Read up on the company values for your behavioral interview
→ At Dataford, we’ve got a list of interview questions from experts to help you prepare.

And last but not least, 𝗬𝗢𝗨 𝗚𝗢𝗧 𝗧𝗛𝗜𝗦!

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The process to land a Data Jobs in Big Tech

How to become a better data analyst:

🔸 Always ask why
🔸 Don’t be scared to say “No”
🔸 Learn and master SQL
🔸 Communicate more
🔸 Build relationships 


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