Category Archives for "Recruitment"

Oct 23

Most Popular Skills and Degrees Among Data Scientists

By ganpati | Recruitment

From a surveying 1400 data scientists working with as many as 220 companies, we can get a much better sense of what types of data scientists companies are hiring, and how senior data scientists differ from their junior counterparts.

Education Levels

While it was typical to see data scientists report multiple degrees, when we looked at the percentages of all distinct bachelor’s, master’s, and doctorate degrees, we found that a little below 50% finished their education with a master’s.

Mar 02

Data Science and Data Analyst Internships

By ganpati | Analytics Career , Recruitment

There are plenty of opportunities! You just have to look in the right places. Many companies won’t have the position “Data Science Intern” listed on their website the same way that Software Engineering internships are listed. In such cases if you take the right initiative and approach them in the right manner it might work in your favour.

Tips to Land an Internship

Be ready with a great resume and cover letter
Once you are asked by an HR or someone from the company to send in your resume it’s best to do that ASAP. You should have at least 2 versions of your resume ready depending on the companies you are focusing on and what they want.

Remember, not all employers are alike when they are taking in an intern. Different companies may have different expectations. It’s best to cater to all your targets.

Also, your resume is not just about you. It should also reflect that your skills and interests are compatible with the company you are trying to apply. So, it’s better to have multiple resume focusing on different aspects of your skill sets.

Aside from resume many employers ask for cover letter. Make sure you have a cover letter ready. Also be ready with some standard questions employers ask to referrers at the time of referral and mail it to the referrer in advance. This will save time for the referrer and certainly give you an edge.

Reach out to your prospective employers

The best way is to have someone who works there and can refer you. In case you don’t have any contact working in the company here are few ways that might work. If you have contacts try to get an informational interview with the hiring manager to assess your fit to the job or even the job profile before applying.

Please be careful about which method should be tried for which company. The recruiter must feel that you have interest in their company, at the same time you must not look desperate to land an internship.

Stay focused: Stick to the plan and apply only to those internships that match your profile, build on your skill matrix and capitalize on the knowledge that you have gained. Anything else may defeat the sole purpose of working hard as an intern. At the end you’ll see, the ones who lost patience and settled for something less have lost an opportunity of fruitful internship.
Explore some extra job sites: Indeed, Glassdoor, Linkedin are the standard channels. Also explore linkup, startuphire, simplyhired, snagajob, Kaggle, incrunchdata, and dice

Try these channels to contact your employer directly

Many positions on icrunchdata and craigslist include a contact’s name and email id, which allows you to follow-up on your submission later. If nothing else works just shortlist some good companies and submit your resume on their websites and you might get lucky.

Used Linkedin as a medium to connect: If you can spare some extra bucks LinkedIn’s paid premium service enables In-mailing the recruiter or hiring manager for some job postings.

Participate in open competitions: this is a fantastic way to get noticed. A better approach is to share some your codes in Git repositories and let prospective employers find you.

Applying for internships announced in job boards: There are several job boards that announce internships from time to time. Though it may require a lot of filtering it’s worth a try to keep yourself updated with these job boards.

Have an Early Start

The preparation for internships should ideally start six months in advance. Utilize the initial 3 months to eliminate the gaps and building a strong resume. You may go through the typical questions asked during an interview and get ready for that. Also, based on the type of jobs you want to apply for, you have to go through some additional preparation.
If you meet the majority of the criteria but not all – but you’re convinced you could do a great job for them, don’t give up. However, don’t try to ignore any obvious gaps. You could try some of the following strategies instead.

  • Identify ways in which you could easily bridge any gaps: “Although I do not currently have Java experience, I have Python and C++ experience and I can pick up fast on a new object oriented programming.”
  • Highlight transferable skills: “Although I have no research background, I have always loved doing data crunching and I’ve participated in online competitions and projects and dealt with huge volume of data.”

You may also like this article from Huffingtonpost on how to make the best of your internship

Sep 12

Analytics Jobs Trend

By Ani Rud | Recruitment

A reliable way to measure the popularity of a technology or tool’s demand in the job market is to measure the number of job advertisements posted with the leading job portals. Going by that assumption here I’ve tried to project the year on year relative growth of 3 most popular analytics tools.

Here’s an interesting statistics that I found from Indeed that shows a steady increase in R jobs compared to SAS but compared to both R and SAS increase in Python jobs since 2008 is simply phenomenal.

The increase in Python jobs can be justified based on the fact that it is a language for lots of quick and dirty procedural work in analytics and at the same time it can be used for building reliable, robust systems. Also, it’s a full fledged programming language with great support.

Relative Trends

Absolute Trends

The very fact that R has surpassed many other technologies like SAS in terms of growth for past couple of years and the closing gap between the absolute numbers shows a possibility that R might emerge as the number two analytics tool in near future. It remains to be seen what 2016 figures has to offer to this analysis.