cnqr

joined 1 year ago
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[–] cnqr 1 points 1 year ago

It’s called A/B testing.

Look it up. Learn something new! What an exciting day it can be for you! It’s an amazing concept.

 

Hey everyone,

We’re entering difficult economic times, so I thought I could share some of the tactics I’ve used to get more job opportunities my way by making my LinkedIn (LI) profile stand out.

I’m not an influencer on LI nor I have insider information about its talent search algorithm. This information comes from reading papers about LI’s search algorithms, researching LI Recruiter, and a lot trial and error experimenting with my own profile.

Let me begin by setting the stage.

To find candidates, recruiters use a tool called LI Recruiter. It allows them to enter relevant search terms such as “Data Scientist” and define filters such as “has worked at Google” to look for candidates.

After a query is defined, LI Recruiter uses a “talent search algorithm” that works in two stages:

1.	It searches the network and defines a set of a few thousand candidates who meet the recruiter’s search criteria.
2.	Then the candidates are ranked based on how well they fit the search term and how likely they are to respond.

That’s it. If your goal is to get more job opportunities your way, then you need to figure out how to improve your chances of appearing in 1 and ranking higher in 2.

Luckily, LI has published research about its talent search algorithm. It’s not hard to get an idea of what will help you stand out from the competition. Based on my research and experience, here are some things that should help your profile stand-out:

1.	Use relevant keywords in your profile. You won’t appear in the results if you don’t include terms in your profile that recruiters use when they search for candidates. Review the keywords used in Job descriptions of the positions you’re interested in, and make sure you have those in your profile.
2.	Reply to recruiters. People often don’t reply to recruiters when they’re not interested in the job  opportunity. But the algorithm prioritizes those who are likely to  respond over those who are not. Respond to recruiters, even if it’s just  to say no!
3.	Grow your network. The lightweight version of LI  Recruiter only lets recruiters reach out to candidates up to their  3rd-degree network. Having few connections decreases your chances of  getting contacted.
4.	Gain influence. You rank higher if you create  engaging content, have many visitors to your profile, or receive  endorsements and recommendations. As a general rule, try to write useful  content periodically and ask for recommendations from relevant  connections.
5.	Make relevant connections. Wanna work at X? Make meaningful connections from X and interact with the brand. When recruiters from X are looking for candidates, you will rank higher.
6.	Use a photo. This is based on my personal experience. A photo, especially a “good” one, increases the likelihood that recruiters will contact you.

If you have any questions, shoot me a message. And just for reference, here’s my profile.

Here are some images and highlights from the papers and research:

LinkedIn Recruiter Lite limits pool of candidates

How LinkedIn talent search works

LinkedIn Recruiter filters

LinkedIn’s talent search architecture

Linkedin’s talent search algorithm

Ranking features

[–] cnqr 1 points 1 year ago

Part III

Sidenote: I got curious about how he’s been writing 50 reviews from 50 different emails per month. Would he actually create 50 different email addresses? And what about the IP address – doesn’t Glassdoor flag multiple reviews from the same IP?

One of the freelancers answered my question:

https://preview.redd.it/g4id2yqeb20b1.png?width=2572&format=png&auto=webp&v=enabled&s=6259b9383e789d3842a682905fc8ada1e3debfaa

Moving on – another company that seems to buy fake reviews seems to be having some more trouble. Approximately a month after a freelancer linked me to fake reviews he had written for this company, all five reviews that he had linked me to had been removed:

https://preview.redd.it/99fdvcgfb20b1.png?width=3116&format=png&auto=webp&v=enabled&s=fd2e8ea258c1f8c448ce69eb55740ea0ae4ecd1a

Based on this Glassdoor webinar from 2018, “if it is found that a user has created multiple email accounts to submit reviews, then ALL submissions from that user are deleted” – so likely Glassdoor’s content moderation team flagged one of the initial reviews and the same freelancer who was writing reviews for that company had all the fake reviews deleted.

So far, it looks like the key to an effective fake review creation strategy lies in:

•	Spacing the fake reviews out
•	Writing each review from a different IP address (i.e benefit of being part of a team)
•	Using language that isn’t an obvious giveaway

On that third point: the reality is that many of these freelancers’ first language is not English.

As an experiment, I turned to everybody’s favorite new toy, ChatGPT, and asked it to write me a positive Glassdoor review:

https://preview.redd.it/8w7cal9gb20b1.png?width=3164&format=png&auto=webp&v=enabled&s=40c0fee4b16081f3566ff463d629dbb8e98c9e8b

And I’d say that the above answer was better than 95% of the fake reviews I came across.

Removing reviews

The process for removing an employer review usually works like this:

1.	You identify one or multiple reviews that you want removed
2.	You verify whether the review violates the site’s Guidelines, or whether there’s something else about the review(s) that could get it removed.
3.	You file an appeal to get it removed.

As an example, Glassdoor’s Review guidelines can be found here. Mainly, they forbid mentioning anyone by name who’s not an executive and revealing proprietary or confidential information, amongst a host of other things.

Sounds simple enough right? Well, according to one of the freelancers I messaged:

https://preview.redd.it/x6s8hsyac20b1.png?width=2036&format=png&auto=webp&v=enabled&s=c4909cd57ee5052a8e29c6752b0345f3f9ccc499

After some research, I summarized the different vendors and prices in the table below:

Channel | Cost | Timeline | Model | Self reported success rate Freelancer #1 | $100 per review | 3 days | Contingency Agreement Model | 100% Freelancer #2 | $30 per review | 7 days | Contingency Agreement Model | 100% Reputation management service #2 | $450 per review | 21 business days | Contingency Agreement Model | Unknown Reputation management service #3 | $1000 per review | Undefined | Contingency Agreement Model | 100% Reputation management service #4 Plan 1 | $550 per review | 5-6 weeks | Contingency Agreement Model | 50-75% Reputation management service #4 Plan 2 | $300 Subscription + $100 per each review removed | Monthly service | Subscription plan | 50-75% Freelancer #3 | $20 | Undefined | Pay regardless | Undefined Freelancer #4 | $500 | Undefined | Contingency Agreement Model | Undefined

As you can see, unlike the fake review generation market, the prices vary quite a bit for getting reviews removed.

At one end, you have freelancers on gig marketplaces that will attempt to remove a review for less than $100. And then on the other end, you have ORMs (Online Reputation Management Agencies) that have multiple employees and more comprehensive packages in place. The one constant seems to be that most companies operate on a contingency agreement model (i.e pay only if review gets removed).

[–] cnqr 1 points 1 year ago (1 children)

Part II

Adding reviews

The barriers to entry for adding fake reviews are much lower than for getting reviews removed, so that’s where we’ll start.

To write an employer review, all you really need is the ability to create an email address. For most sites, you don’t need any proof of employment (say like a company specific email address).

I went on a gig marketplace site and posted a pretty vague post related to wanting to find out more on how to improve a company’s online presence.

Within minutes of posting a gig, my inbox was flooded with proposals:

https://preview.redd.it/esx3904qa20b1.png?width=3064&format=png&auto=webp&v=enabled&s=7579e9121fae3788cafa3b3860b88b2526b4807a

After a bit of chatting, I narrowed the scope of their services and summarized their rates into the table below:

Channel | Cost | Timeline | Model Freelancer #1 | $10 per review | Monthly | Unlimited Freelancer #2 | $35 per original review, $20 per already created review | Monthly | Unlimited Freelancer #3 | $25 per review | Monthly | Unlimited Freelancer #4 | $25 per review | Monthly | 10 reviews Freelancer #5 | $20 per review | Monthly | Unlimited Online Reputation Management Agency | $300 subscription | Monthly | 8 reviews

Let’s dive a bit deeper into the services that Freelancer #5 offered.

Freelancer #5 explained to me he had been writing reviews for one particular company for the past 4 months now. Each month he wrote them 10 reviews.

https://preview.redd.it/n1ddox6cb20b1.png?width=2684&format=png&auto=webp&v=enabled&s=2ef0b51da38b78eefd1eff693484ee4d770f3ae6

In another message, he tells me he’s offering the same services to 5 other companies. Doing some quick math:

5 companies x 10 reviews per company x $25 per review = $1,250 per month

Considering the average person in Pakistan earns $150 per month, that’s not bad change at all.

One of the companies that he’s offering his services to includes a Y-Combinator backed startup. I won’t name the company, but here’s what its average Glassdoor review rating distribution looks like:

https://preview.redd.it/2np5b6fdb20b1.png?width=2420&format=png&auto=webp&v=enabled&s=81b9cc35226baf4ff8be0b199358958632fcdc5b

5 star reviews account for over 77% of the company’s total reviews. Obviously, no one is buying fake reviews that make them look bad.

But here’s the thing: freelancers are getting quite smart when it comes to writing reviews that don’t look too fishy. They tend to do this by spacing the reviews out (so that they don’t come in “spikes” – more on this later) and they also make sure that they’re not always leaving the “cons” section blank.

Don’t get me wrong, if you come across this company’s reviews, it’d be pretty easy to tell they’re quite strange. In fact, I can’t even post some screenshots here because it’d give the company away immediately.

But it would be challenging to conclude that the above company is buying reviews just by analyzing review volume and distribution without actually reading some of the reviews.

The same company is also buying reviews on Google Reviews.

 

Part I

Online company reviews are high stakes.

Top reviews on sites like Glassdoor and Google can get thousands of impressions each month and are major drivers of brand perception.

Employers know this. And when I come across multiple 5 star reviews left with no cons, or a Pulitzer worthy essay from a former intern, I become suspicious.

These reviews start to resemble 30 under 30 lists: so artificially constructed that you begin to question their credibility in the first place.

The scrutiny around company reviews is well documented; some companies file lawsuits worth over a million dollars to reveal anonymous reviewers that complain about their jobs.

Whilst it’s the flashy lawsuits that make the headlines, there also exists an underground economy of company reviews operating quietly every single day.

In this underground economy, some companies pay over $150 to freelancers to try and get a negative review removed. If they want “better” results, they go to the plethora of Online Reputation Management services (ORMs) in the United States that can charge retainers worth thousands of dollars.

The supply of positive reviews exists too. My research led me to find companies, including a prominent Y-Combinator backed startup, that solicit fake positive reviews from online freelancers to improve their rating.

Many of these mercenary fake reviewers, often based in South East Asia, make a full time living doing this, netting over $2,000 per month.

Some of these run such sophisticated operations that they’ve even created their own pricing tiers (e.g $35 per original review, $20 to post an already created review from an email address), a la SaaS offering.

Others operate on a contingency fee agreement model, where they only get paid if they’re able to take a negative review down.

The underground economy of company reviews is well and truly alive. And today we’re going to find out how it operates.

Note: For more content like this, subscribe to my newsletter. In a couple of weeks, I’ll be releasing my guide to writing a killer resume.

 

I’ve recently launched “PYTHON CHARTS”, a website that provides lots of matplotlib, seaborn and plotly easy-to-follow tutorials with reproducible code, both in English and Spanish.

Link: https://python-charts.com/ Link (spanish): https://python-charts.com/es/

https://preview.redd.it/v4kwjk5hn0x91.png?width=939&format=png&auto=webp&v=enabled&s=e873096bd8d2855c97cc02d5d3267bdfce2b3ccc

The posts are filterable based on the chart type and library:

https://preview.redd.it/4tfvn5prn0x91.png?width=898&format=png&auto=webp&v=enabled&s=041fb67fd1aac587b51754a59549d9885f4c7d1d

Each tutorial will guide the reader step by step from a basic to more styled chart:

https://preview.redd.it/yrsnxpdwn0x91.png?width=694&format=png&auto=webp&v=enabled&s=8cdd4c01bf8915afad33910e6fa9c7bb533ddb76

The site also provides some color tools to copy matplotlib colors both in HEX or by its name. You can also convert HEX to RGB in the page:

https://preview.redd.it/hxhdctl2o0x91.png?width=890&format=png&auto=webp&v=enabled&s=d8cc8f65a15cb49876b314bc442fd8deae0da547

•	I created this website on my spare time for all those finding the original docs difficult to follow.
•	This site has its equivalent in R: https://r-charts.com/

Hope you like it!

 

Hey everyone. I posted a thread a few days ago about being nervous about my first DS interview. The thread was taken down by mods due to it being more appropriate for the stickied thread. So I want to make this thread less about questions, but more of an informative post to show you some of the questions I was asked. Hopefully it’s helpful for newbies and veterans alike!

SQL:

•	What is a view?
•	Is a table dynamic or static?
•	Difference between a primary key and foreign key
•	Inner Join vs. Left Join scenario (pretty sure it was from w3schools. ez pz)
•	WHERE vs. HAVING
•	When would you use a subquery? Provide an example
•	How would you improve the performance of a slow query?
•	EDIT: Some aggregation and GROUP by questions (MAX, AVG, COUNT, etc.) that I just remembered.

Python

•	Explanation of libraries I use (Pandas mainly)
•	How would you get the maximum result from a list?
•	Can you explain the concept of functions
•	Difference between FOR and WHILE loops?
•	Give some examples of how you would clean dirty data.

Tableau:

•	What is a calculated field? Provide some examples in your work
•	What is the difference between a live view and extract? When would you use each?
•	More information given on the data I work with

Statistics:

•	Explain what a p-value is to someone who has no idea what that is.
•	Explanation on linear/logistic regression modeling.
•	What is standard deviation? Examples?
•	Difference between STDEV and Variance?
•	What statistics do you currently work with? (Descriptive mainly… mean, median, mode, stdev, confidence intervals)

I advanced to round 3 immediately, which is pretty much a shoe-in according to the hiring manager. I am very excited because it seems like a great opportunity. Even if I don’t get it, I still felt like I interviewed very well and did my best. I am very proud of myself.

120k a year w/ benefits, bonuses, and training courses a week to help me learn more advanced DS concepts, Python, or whatever I want. I am so excited.

 

I’ll be manually copy pasting (with references) some useful posts from the past year in r/DataScience to get the things rolling in this instance.