Category Archives for "Analytics Career"

Jul 17

Data Science Falls Into Many Roles

By ganpati | Analytics Career

job roles
Forbes
Rawn Shah

Data science continues to grow in significance in industry, particularly in industries like software, IT consulting and finance. Last year I shared results from O’Reilly Media’s annual salary survey in this field in Revealing Data Science’s Job Potential. They have just recently released results for their third annual Data Science Salary survey and here are some of their findings.

Over 600 people completed the survey when the questions were opened to anyone, of which the majority (67%) was from the U.S. The data allows a closer look by U.S. regions, particularly California and the Northeast.

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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

Jan 28

Role of Data Scientists in Data Driven Companies

By ganpati | Analytics Career

Fast growing, data driven companies have different hues of data and hence there are different flavors of data scientists than you might think. There are data scientists working on improving user experience, who don’t only rely on data but also behavioral traits of users as uberdata suggests. And then there are data scientists working on image recognition who may employ advanced machine learning techniques to distinguish a cat from a human purely on the basis of algorithms.

So in a large company that deals with a variety of data, there is no standard job description for a data scientist. The roles may vary and so do the required skill set. Here are some areas in a data driven company where data scientists are involved and the skill set that each job requires:

The Growth DS

The Growth DSs partner with the Marketing team and the Product team to identify ways to drive more traffic to the app or portal. They usually focus on: connections, network, long term user engagement, types of user engagement, dormant member resurrection, new member registration, and referrals. They discover areas of opportunity, outline how to close the gap, guide product design, and analyze impact after product release.

There may be so many different acquisition channels involving other platforms, so the job of these data scientists is to help build pipelines that link company’s data with the third parties. These data scientists also help design and analyze scalable experiments with both offline (not tracked at a user level) and online (more traditional A/B experiments) tests.

The Product DS

The second category of data scientists focus on analyzing changes to the core product, including search rankings and price recommendations.

For example for a hotel booking company they’ll often use datasets with logged search queries, calendar preferences, and booking flow interactions. These projects tend to be a mix of experimental analysis and product development (with machine learning models and simulations).

The challenge for this category of scientists is to keep things simple. It’s easy to propose a complex solution. It’s hard to come up with a simple one. You may come up with a complex model but many a times you have to convey the business logic behind the model to a non-technical person. Always keep these 3 rules in mind while deciding on a product feature: Simplicity Makes People Happy, Simplicity Makes People Think Better, Simplicity Makes People Spend Money. By channeling your thought process like a business person, you’ll be able to avoid many of the common mistakes.

Transaction DS

Then comes the transactional DS or behavioral DS who mostly work on developing models that use machine learning techniques to identify anomalies in transaction data and user behavior data, respectively. These data scientists are also responsible for analyzing any product changes associated with these models, like payment options or verified identifications. In addition to working with the Product team, these data scientists will also often partner with operations teams, like Sales representatives, fraud analysts and security teams.

Operational DS

Lastly, the operational DSs who partner with the rest of the departments.

For example an operational data scientist will interact with policy, finance, sales, billing, marketing and human resources.

For example, some companies would calculate a Net Promoter Score (NPS) and try to find out which are the 3 steps that can be taken to increase the the score. Now, these steps can’t be taken in isolation by a particular department of the company. Multiple departments have to coordinate and act in sync to implement such steps. This is where the operational data scientists or core analytics team come into picture. In addition to having the machine learning skills these people need to have a good understanding of the business and look at ideas from a practical angle and they should also have good people kills to interact and convince others about these ideas.

These data scientists tend to rely more on third-party sources (like surveyed, census, or employee data) and less on experimental data.