Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...
Full BioNand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
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The Two Sides of Getting a Job as a Data Scientist


- Some people have no idea what Data Science is. So study the company you are applying for, see what their employees are doing, look for the way the communicate, their Facebook, LinkedIn, Twitter, talks and webinars. And see if they are doing something that interests you.
- The recruiter is your best friend at the moment of interviews, they want to help you get in. So trust them, let them help you and ask questions!
- People are generally more interested in how you solve problems and how you deal with some specific situations than your technical knowledge. Of course is important to write good quality code and have a full understanding of what you are doing, but there's more than that.
- Be patient. You will apply for maybe hundreds of job before getting one (hopefully not).
- Prepare. A lot. Not only studying important concepts, programming and answering business questions, also remember that you will be an important piece of the organization, you will deal with different people and situations, be ready to answer questions about how would you behave in different work situations.
- Have a portfolio. If you are looking for a serious paid job in data science do some projects with real data. If you can post them on GitHub. Apart from Kaggle competitions, find something that you love or a problem you want to solve and use your knowledge to do it.
- The recruiter is your friend. The people interviewing you too. They want you to get in the company, that's a powerful advise that I remember everyday.
- Ask people about what they do. I recommend that you follow Matthew Mayo post on "A day in the life of a Data Scientist" to have a better idea of what we do.
- If you want an internship, have your academic skills on point.

If you have something you want the reader or listener [interviewer] to know, you'd better put that up front in your message. For resumes, that means you lead with your strongest aspect. Maybe that's your education. Maybe it's your job experience.Don't feel that you have to follow the order in that resume template you downloaded.
When an interviewer asks, "Tell me about yourself", you don't need to give them a chronological account of your life story. Start by telling them what your #1 strength is.
You want to communicate your passion for the field? Do some personal projects. Contribute to open source. Start a blog. Heck, be active [...] on LinkedIn.
Words are cheap; actions are what counts.
And in our competitive field, you want to avoid doing anything that will cause people to not take you seriously.
Turn every bullet point on your resume into a mini story. You've probably already got a full page of text, and it's probably cluttered with one-sentence junk that says "I did this" or "we did that." Go ahead and delete half of that.
Now that you've freed up some space, start expanding on the remaining accomplishments.
Use the STAR format to give each bullet point context and to turn it into a detailed mini story with a resolution.
It's better to have a few standout stories and accomplishment on your resume than a whole lot of "stuff."

- Be honest. Don't undersell or oversell your self in your resume.
- Connect and be active in the data science community. Create blogs, share your knowledge, participate in open source projects.
- Be clear. Read your resume and ask yourself: is this how I want to be seeing?, be sure that you are putting the things that you think are the most important for you and the company you are applying for in the begining.
- Don't send the same resume to every company. This is very close to the last point, and it's a hard job. But believe me you'll see results much faster. Analyze the company and create a resume specific for that position.
- Keep it short. They get thousands of resumes everyday, so they will only expend around 30â??60 seconds reading yours. So be sure that you are putting there the things they want to see. Don't put there stuff that is not relevant for the company.
- Be consistent. That means same font and style everywhere.
- Tell your story. Those bullets you see in your resume are you. So tell the story of your life in a way you and them will like it. If you are stronger on academic skills be sure to put that before the experience part, or vice versa.
- Ask the recruiter for advise before sending the resume.

- A phone call where they will ask you about you and your experience. This is the first phone screen.
- If everything goes well you'll get a second call, this time maybe from some Data Scientist that work in the company. This is the second phone screening. They will ask you more about you, your experience and also some technical questions. This is more likely to see if the things you said in your resume are true.
- (Optional) Data science task. They'll send you a dataset and ask you several questions to see your abilities as a data scientist. Be really clear here. Write good quality code.

A Data Scientist is a person in charge of analyzing business problems and give a structured solution starting by converting this problem into a valid and complete question , then using programming and computational tools develop codes that clean, prepare and analyze the data to then create models and answer the initial question.


... leveraging a data science team appropriately requires a certain data maturity and infrastructure in place. You need some basic volume of events, and historical data for a data science team to provide meaningful insights on the future. Ideally your business operates on a model with low latency in signal and high signal to noise ratio.
- Recruiters, work closely with hiring managers to build out accurate job descriptions.
- Iron out nuances to distinguish which types of data scientists will be the best fit for the business' needs. Hone in on the skillset and experience of the type of data scientist you're looking for.
- Think long term. Understand how the org plans to leverage this role within the product roadmap.
- Set realistic expectations of available candidate pool. There are more roles than candidates, so recruit accordingly.
- Build a list of ideal candidates and calibrate with hiring manager to gauge fit against reality of talent market.
Aspiring data scientists want 1 thing from the companies that don't hire them: an explanation. In many cases their only response is silence. How's an aspiring data scientist supposed to know what to work on, if companies won't tell them?
Aspiring data scientists aren't psychics, but they are hardworking & willing to learn. They'll rise to the challenge if companies start telling them where the bar is.
Peel back the hiring process at most companies & you'll find they can't objectively answer the question, "Why didn't you interview or hire this person?" I teach clients how much they can learn by examining the candidates they reject as closely as they examine the people they hire.
There's value to both candidates & employers in the answer to that question. Companies have an opportunity to improve their hiring process. Candidates get the opportunity to be better prepared for their next application with the company.
Beyond the value, it's the decent thing to do for someone who took the time to apply. Hiring is all about making connections. Silence shows the company doesn't care enough to treat people the right way. That's something candidates remember.