u/Buzzkill_Kangaroo

I’m currently more of a SaaS Product Manager, but yesterday I got stuck thinking about moving toward an analyst-type role: where to start, what skills to focus on, and what the current market actually asks for.

So I did this:

I copied around 70 job posts from a niche job board that posts roles from solid international companies.

Then I gave the posts to Claude and asked it to:

1. Extract all mentioned companies.
2. Find current or recently archived Product Analyst roles at those companies.
3. Build a skills frequency table based on those job descriptions.

Something like:

SQL — mentioned in 100% of roles
A/B testing — mentioned in 70%
Python — mentioned in 80%
BI tools — mentioned in 100%

Then I asked it to break down each skill into subtopics and suggest where to learn them.

For example:

Statistics → confidence intervals, hypothesis testing, statistical significance
A/B testing → experiment design, sample size, MDE, interpretation
Python → pandas, notebooks, data cleaning, analysis workflows
Product analytics → funnels, cohorts, retention, LTV, ARPU

And then for each topic, I asked for practical resources: courses, YouTube videos, simulators, bootcamps, and project ideas.

The output was surprisingly useful. It turned a vague “I should learn analytics” into a very concrete roadmap based on actual job descriptions.

The main takeaway: don’t ask AI “how do I become an analyst?”
Give it real job posts from companies you actually care about, then ask it to reverse-engineer the skill map.

written by human, redacted by ai

I’m currently more of a SaaS Product Manager, but yesterday I got stuck thinking about moving toward an analyst-type role: where to start, what skills to focus on, and what the current market actually asks for.

So I did this:

I copied around 70 job posts from a niche job board that posts roles from solid international companies.

Then I gave the posts to Claude and asked it to:

1. Extract all mentioned companies.
2. Find current or recently archived Product Analyst roles at those companies.
3. Build a skills frequency table based on those job descriptions.

Something like:

SQL — mentioned in 100% of roles
A/B testing — mentioned in 70%
Python — mentioned in 80%
BI tools — mentioned in 100%

Then I asked it to break down each skill into subtopics and suggest where to learn them.

For example:

Statistics → confidence intervals, hypothesis testing, statistical significance
A/B testing → experiment design, sample size, MDE, interpretation
Python → pandas, notebooks, data cleaning, analysis workflows
Product analytics → funnels, cohorts, retention, LTV, ARPU

And then for each topic, I asked for practical resources: courses, YouTube videos, simulators, bootcamps, and project ideas.

The output was surprisingly useful. It turned a vague “I should learn analytics” into a very concrete roadmap based on actual job descriptions.

The main takeaway: don’t ask AI “how do I become an analyst?”
Give it real job posts from companies you actually care about, then ask it to reverse-engineer the skill map.

How do you use AI for career advice? Has it ever changed your plan?

I’ve been testing a few lead gen tools lately, and I keep running into the same problem.

They’re pretty good at building static lists.

Industry, headcount, location, tech stack, funding stage, job titles — all useful.

But I still end up doing the most important part manually:

finding a real reason to reach out.

For example:

- they just hired a VP Sales
- they’re hiring for a role related to our product
- they raised recently
- they switched tools
- they’re expanding into a new market

That’s the stuff that makes outreach feel relevant.

But right now, getting those buying signals usually means opening a bunch of tabs: LinkedIn, company websites, job boards, funding databases, Google, etc.

The lead list is fast.
The research is still slow.

That’s the part I’m trying to simplify.

Not just finding companies that match an ICP, but finding companies that already have a fresh trigger attached.

Just a list of prospects with the reason included.

Still validating this before building too much.

Curious how others handle this:

How are you finding buying signals today?

Is this still mostly manual for you?

And if you’ve tried AI lead gen tools recently, what was the dealbreaker — data quality, pricing or smth else?

I’ve been testing a few lead gen tools lately, and I keep running into the same problem.

They’re pretty good at building static lists.

Industry, headcount, location, tech stack, funding stage, job titles — all useful.

But I still end up doing the most important part manually:

finding a real reason to reach out.

For example:

- they just hired a VP Sales
- they’re hiring for a role related to our product
- they raised recently
- they switched tools
- they’re expanding into a new market

That’s the stuff that makes outreach feel relevant.

But right now, getting those buying signals usually means opening a bunch of tabs: LinkedIn, company websites, job boards, funding databases, Google, etc.

The lead list is fast.
The research is still slow.

That’s the part I’m trying to simplify.

Not just finding companies that match an ICP, but finding companies that already have a fresh trigger attached.

Just a list of prospects with the reason included.

Still validating this before building too much.

Curious how others handle this:

How are you finding buying signals today?

Is this still mostly manual for you?

And if you’ve tried AI lead gen tools recently, what was the dealbreaker — data quality, UX, pricing, or smth else?

[ Removed by Reddit on account of violating the content policy. ]

I’ve been testing a few lead gen tools lately, and I keep running into the same problem.

They’re pretty good at building static lists.

Industry, headcount, location, tech stack, funding stage, job titles — all useful.

But I still end up doing the most important part manually:

finding a real reason to reach out.

For example:

- they just hired a VP Sales
- they’re hiring for a role related to our product
- they raised recently
- they switched tools
- they’re expanding into a new market

That’s the stuff that makes outreach feel relevant.

But right now, getting those buying signals usually means opening a bunch of tabs: LinkedIn, company websites, job boards, funding databases, Google, etc.

The lead list is fast.
The research is still slow.

That’s the part I’m trying to simplify.

Not just finding companies that match an ICP, but finding companies that already have a fresh trigger attached.

Just a list of prospects with the reason included.

Still validating this before building too much.

Curious how others handle this:

How are you finding buying signals today?

Is this still mostly manual for you?

And if you’ve tried AI lead gen tools recently, what was the dealbreaker — data quality, UX, pricing, or smth else?

I’ve been testing a few lead gen tools lately, and I keep running into the same problem.

They’re pretty good at building static lists.

Industry, headcount, location, tech stack, funding stage, job titles — all useful.

But I still end up doing the most important part manually:

finding a real reason to reach out.

For example:

• they just hired a VP Sales
• they’re hiring for a role related to our product
• they raised recently
• they switched tools
• they’re expanding into a new market

That’s the stuff that makes outreach feel relevant.

But right now, getting those buying signals usually means opening a bunch of tabs: LinkedIn, company websites, job boards, funding databases, Google, etc.

The lead list is fast.
The research is still slow.

That’s the part I’m trying to simplify.

Not just finding companies that match an ICP, but finding companies that already have a fresh trigger attached.

Just a list of prospects with the reason included.

Still validating this before building too much.

Curious how others handle this:

How are you finding buying signals today?

Is this still mostly manual for you?

And if you’ve tried AI lead gen tools recently, what was the dealbreaker — data quality, UX, pricing, or smth else?

I had to compare us against 3 competitors for a strategy doc this week.

Claude gave me a pretty clean matrix, and honestly the structure was useful. but when I spot-checked a few rows, it got messy fast:

• a couple pricing tiers were wrong
• one thing was outdated
• one feature claim I couldn’t verify anywhere
• one G2 quote didn’t seem to exist

So I probably saved 20–30 minutes on formatting, but then spent about about 2 hours checking the claims before I felt okay using any of it.

maybe this is a prompting issue, or maybe I’m expecting too much here. I’ve tried Claude’s research mode in chat, and also Claude Code’s plan mode running locally
I payed $100 for the Max Plan.

The weird part is that a few times, after I pushed back, it basically said:
“Good catch, checking again…” and after 5-10 minutes of thinking “I was wrong.”

At some point I realized that the deeper I dug into the finished report, the more issues I found. it started to feel endless.

How do you guys handle it?

I had to compare us against 3 competitors for a strategy doc this week.

Claude gave me a pretty clean matrix, and honestly the structure was useful. but when I spot-checked a few rows, it got messy fast:

• a couple pricing tiers were wrong
• one thing was outdated
• one feature claim I couldn’t verify anywhere
• one G2 quote didn’t seem to exist

So I probably saved 20–30 minutes on formatting, but then spent about about 2 hours checking the claims before I felt okay using any of it.

maybe this is a prompting issue, or maybe I’m expecting too much here. I’ve tried Claude’s research mode in chat, and also Claude Code’s plan mode running locally
I payed $100 for the Max Plan.

The weird part is that a few times, after I pushed back, it basically said:
“Good catch, checking again…” and after 5-10 minutes of thinking “I was wrong.”

At some point I realized that the deeper I dug into the finished report, the more issues I found. it started to feel endless.

How do you guys handle it?