Try not to exaggerate your skills. If the job sounds more engineering focused than you are wanting, be honest and say that. Data Science is getting very broad and you don’t want to get in a position that is a bad fit.
You often sound worse when you try to explain something you do not understand. Just be honest and say, “I have not needed to use that yet, can you explain to me when you have done that?”
Find the job you are looking for, not just any position someone is trying to fill.
2. Tell Stories of Your work
Talk about things you have built. If you built something as part of a team or project, tell about why and your involvement.
If you have a side project, talk about that. This is why side-projects are so important. They help you learn a lot, plus they give you something exciting to talk about, which only you can talk about.
Interviews are intimidating. There is really no way to avoid that. The best you can do is be prepared for the interview. Pramp provides a platform for practicing data science interviews. Practice makes perfect.
The year 2019 has begun and many people have plans to become a data scientist. That is because data scientist has been ranked as one of the top jobs for the last several years. After learning the necessary skills, preparing and completing the interview can be an intimidating task. That is why interview practice is so important, and Pramp provides a free online environment for practicing a data science interview.
What is Pramp?
Pramp is a free peer-to-peer matching platform that enables you to practice a technical interview. Aftersigning up, here is how the process works:
You schedule an interview by choosing a date and time for when you would like the interview to occur.
You then prepare for the interview with the materials Pramp provides. Pramp will supply interview questions and guidelines for being best prepared.
Finally, you conduct the interview where you and the other person take turns interviewing each other.
If desired, the process can be repeated multiple times.
Pramp takes two people preparing for a data science interview and matches them together.
Being on both sides of the interview is surprisingly very helpful. It allows you to practice your responses, and it allows you to understand what is important to the person asking questions. It is often more about understanding the problem and thinking through a solution, rather than identifying a right or wrong answer.
As a bit of a bonus, if you enjoyed interviewing with your peer and you’d like to practice with him/her again, Pramp has a feature for that. Who knows, that peer may become a friend or a coworker in the future.
Why Practice the Interview?
Even the best data scientists and engineers struggle to pass technical interviews. Let’s face it, technical interviews are challenging and intimidating. For many, the biggest challenge isn’t the coding question, but rather staying focused while solving a problem out loud and under time pressure in front of an interviewer.
Data from over 180,000 interviews scheduled on Pramp has shown that those who completed face-to-face mock interviews performed significantly better than those who just practiced alone. Plus, Pramp users have already found jobs at companies like: Google, Amazon, Facebook, Twitter, Microsoft, Spotify, and many others.
More About Pramp
Pramp, a Y Combinator-funded company, has tackled the challenge of technical interviews by offering a free peer-to-peer mock interview platform helping data scientists and engineers practice technical interviews. In addition to data science, Pramp also offers interviews for:
iCrunchData, one of the most popular data science job sites, keeps an index of the data science job market. Recently, the index just passed 500,000 big data jobs posted online. That is a phenomenal number, and it just goes to show the massive need for more people with big data skills. Also of note, analytics jobs are at nearly 250,000 and even statistics jobs are approaching 70,000 according to the index.
An entry level data analyst should expect a yearly salary in the range of $50,000 to $75,000. A more experienced data analyst should expect as high as $110,000.
The range for a data scientist goes from $85,000 up to $170,000.
An analytics manager, depending upon the number of direct reports, can command a salary up to $240,000 for 10 or more directs.
A big data engineer can expect a salary of $70,000 to $165,000, depending upon level of experience and the company.
If you have the right skills, right now is an excellent time to find a big data job. If you don’t yet have the skills, it is a good time to start learning because the current trend of open big data jobs is showing no signs of slowing down.
The Central Intelligence Agency is hiring data scientists. They appear to be hiring at multiple levels. Unfortunately, the position description is quite vague. It is tough to know exactly what the CIA is looking for and technologies are currently being used at the CIA.
Kaggle Prospect In this competition, the participants are trying to come up with the best question to ask. Participants are presented with various related datasets, and the goal is to find which data science question should be asked of the data. The winner gets a small cash prize, and the winning question becomes a regular kaggle competition.
What do you think? Are you excited to try out these new competitions?
This report by McKinsey & Company is frequently referenced, so I thought I should post a link to it. It includes the following quote about the lack of talent to fill Big Data positions.
By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.
This quote is why now is a great time to be learning to become a data scientist.
The questions are similar, but if you read closely, they are different. I wrote some Java code to answer those questions. The raw results are posted here.
Honestly, nothing too surprising showed up. Not counting the common English words (and, to), the word data was the most popular. It occurred 167 times and it occurred at least once in all 16 job postings. That makes sense; a data scientist should know about data. I thought hadoop would occur in all job descriptions but it only appeared in 11 of the 16 job descriptions. Here are some other words I found interesting:
statistical occured 29 times and in 10 job descriptions
analysis occured 46 times and in 13 job descriptions
analytics occured 22 times and in 6 job descriptions
statistics occured 16 times and in 9 job descriptions
machine learning occured 14 times and in 9 job descriptions
phd occured 11 times and in 11 job descriptions
sql occured 12 times and in 10 job descriptions
On an interesting note, Python and R occurred in more job postings than Java (2 more to be exact).
Does anything in the results strike you as interesting?