Issue #16: Handpicked Entry-Level Jobs & Internships + Laurel or Yanny? (It’s Laurel.)

Hey there,

Whether you’re still taking your finals, or just graduated, we’re proud of you and here to help you with those next steps.

Let’s get to it!

The Uncubed Team


League Apps

Slack is known for being one of the most well-used communications tools in companies, especially start-ups.

Slack has 8 million daily active users, 65 companies from the Fortune 100 as users, and over 500,000 organizations use Slack.

You can be a part of the company that allows companies to easily communicate back and forth, and made it easier to react, respond, and collaborate at work.

Pretty cool, right? The best part is that Slack is hiring for a ton of roles across departments, and throughout the U.S. and internationally.

👉 Learn more + apply here. 👈


  1. Sr. Intern Avionics – @ Lilium (Munich, Germany)
  2. Technical Solutions Engineer – Intern (San Jose, CA)
  3. Tech Recruiting / Talent Acquisition Intern – @ Uncubed (New York, New York)
  4. Data Scientist – Intern 2018 @ Quora (Mountainview, CA)
  5. Data Analyst/Scientist Intern @ Endurance International Group – (NYC)


  1. Data Scientist @ Hoteltonight (New York, New York)
  2. Junior Product Designer @ Newsla (NYC)
  3. Junior Data Analyst @ Smarkets (London UK)
  4. Junior Software Engineer @ Jampp (Buenos Aires, Argentina)
  5. Software Engineer – Platform @ Animoto (NY)



This question comes from Dice.com

Question: “What is the difference between bias and variance?”.

What Most People Say:

“Bias and variance is supposed to set the algorithm designer,” or “Bias of an estimator is the difference between the estimator’s expected value and the value of the parameter being estimated. Variance of an estimator is a measure of how far values of the estimate can take away from its value.”

What You Should Say:

“Bias comes as a consequence of a model underfitting some set of data, whereas variance arises as the result of overfitting some set of data.”

Why You Should Say It:

It’s a simple answer, but avoids the textbook responses given by many candidates. Interviewers want to know if you understand the impact that overfitting or underfitting can have on a machine learning application. Developers who want to work on machine learning need to have a firm understanding not only of coding, but of each of the components that go into creating a successful machine-learning application.


Question: How familiar are you with [specific programming language]?

Answer: In most cases, your interviewer is checking to see if you’re comfortable with the language. You don’t need to be a senior-level expert. In fact, you want to avoid coming across as a specialist. Don’t say, “I prefer Ruby and don’t really know Java.” Instead say, “I’m really familiar with Ruby and am happy to dive into learning more Java. I love working in new languages.”

Be confident, and don’t get hung up on your skill level per language. Instead, emphasize how well-versed you are in coding as a skill, and how well you solve problems. Remember, tech companies are up against a tech talent shortage, so they’re not looking for perfection, but rather competency and drive.

Click here for more tips from this thread.


This week’s tip comes from Alan Collins via LinkedIn: “I’ve sent out 200 HR resumes and only got one interview. What am I doing wrong?”

Here’s his advice.



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See you next Wednesday!
The Uncubed Team