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

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/

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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

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• 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!

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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.