{"id":1322,"date":"2019-02-15T10:32:04","date_gmt":"2019-02-15T07:32:04","guid":{"rendered":"http:\/\/kusuaks7\/?p=927"},"modified":"2023-08-09T06:36:14","modified_gmt":"2023-08-09T06:36:14","slug":"the-two-sides-of-getting-a-job-as-a-data-scientist","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/the-two-sides-of-getting-a-job-as-a-data-scientist\/","title":{"rendered":"The two sides of Getting a Job as a Data Scientist"},"content":{"rendered":"<p><strong><em>Ready to learn Data Science? <a href=\"https:\/\/www.experfy.com\/training\/courses\">Browse courses<\/a>\u00a0like\u00a0<a href=\"https:\/\/www.experfy.com\/training\/tracks\/data-science-training-certification\">Data Science Training and Certification<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p>\n<h4 style=\"margin-left: -1.3pt;\">Are you a Data Scientist looking for a Job? Are you a Recruiter looking for a Data Scientist? If you answered yes or NO to this questions you need to read\u00a0this.<\/h4>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 600px; height: 338px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*tnCD0CvJnP3JkWU6In9TGQ.jpeg\" alt=\"experfy-blog\" \/><\/p>\n<p>Hello! This is a blog post I\u2019ve been waiting a lot to write. Mostly because I needed to do my research and listen to what other people have to say about this.<\/p>\n<p>I consider myself a Data Scientist, not a recruiter, but thanks to an amazing conversation I had with\u00a0<a href=\"https:\/\/www.linkedin.com\/in\/amandavoss0304\/\" target=\"_blank\" rel=\"noopener noreferrer\">Amanda Voss<\/a>\u00a0and more recruiters in the area of Data Science and IT I have an idea now of both sides of the story: the DS looking for a job, and the recruiter looking for the best DS for a position.<\/p>\n<p>Before starting, if you want to know more about my personal experience looking for a job as a Data Scientist read my blog: \u201cHow to get a job as a Data Scientist?\u201d<\/p>\n<p style=\"text-align: center;\">\n<h2 style=\"margin-left: -1.6pt;\"><strong>The Data Scientist side<\/strong><\/h2>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 600px; height: 364px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*jpJOYrvDu4SpiGUBvbJwOQ.jpeg\" alt=\"experfy-blog\" \/><\/p>\n<p>So you are a Data Scientist, or you think you are getting closer to be one, and you started looking for a job in the area. My first suggestion:\u00a0<strong>be patient!<\/strong>\u00a0This is not an easy task, and maybe you will apply to hundreds of job before getting one.<\/p>\n<p>Of course, it could be really easy and fast process, but in my experience this will take you at least (approx) 100 applications and several months.<\/p>\n<p>Learn from each application, each rejection. When I started applying for jobs I had to deal with a lot of rejection. Something I was actually not prepared to. I think no one prepares you for rejection, but if you get something from this is, it\u2019s ok! it\u2019s normal and not personal!<\/p>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 600px; height: 424px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*kQpLavQ9uExumBoqVBNLiw.jpeg\" alt=\"experfy-blog\" \/><\/p>\n<p>Every rejection is a step on your way to success. It\u2019s not easy to fit in every characteristic that the employer may want, or have the right experience, or maybe just cultural fit.<\/p>\n<p>If you are lucky enough you will get an amazing recruiter that will let you know what happened and how you can improve for future interviews or processes. Recognizing your flaws and weaknesses is the beginning of getting better. This frustration you feel now, or you may feel use it to improve and get better every time.<\/p>\n<h2 style=\"margin-left: -1.2pt;\"><strong>Three Key points to have in mind about the\u00a0process<\/strong><\/h2>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 600px; height: 390px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*AIYxXE7kdnCyXrLX9ZoCug.jpeg\" alt=\"experfy-blog\" \/><\/p>\n<ul>\n<li>Some people have no idea what Data Science is. So<strong>\u00a0study the company you are applying for<\/strong>, see what their employees are doing, look for the way the communicate, their Facebook, LinkedIn, Twitter, talks and webinars. And\u00a0<strong>see if they are doing something that interests you.<\/strong><\/li>\n<li>The recruiter is your\u00a0<strong>best friend<\/strong>\u00a0at the moment of interviews,<strong>\u00a0they want to help you get in<\/strong>. So<strong>\u00a0trust them<\/strong>, let them help you and\u00a0<strong>ask questions<\/strong>!<\/li>\n<li>People are generally more interested in how you\u00a0<strong>solve problem<\/strong>s and how you<strong>\u00a0deal with some specific situations\u00a0<\/strong>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\u2019s more than that.<\/li>\n<\/ul>\n<h2 style=\"margin-left: -1.2pt;\"><strong>Some advice to get a job as a Data Scientist<\/strong><\/h2>\n<h2 style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 600px; height: 338px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*HWrUR0wj4ryiosFxxDLqPQ.jpeg\" alt=\"experfy-blog\" \/><\/h2>\n<ul>\n<li><strong>Be patient.<\/strong>\u00a0You will apply for maybe hundreds of job before getting one (hopefully not).<\/li>\n<li><strong>Prepare<\/strong>. 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.<\/li>\n<li><strong>Have a portfolio<\/strong>. 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.<\/li>\n<li><strong>The recruiter is your friend<\/strong>. The people interviewing you too. They want you to get in the company, that\u2019s a powerful advise that I remember everyday.<\/li>\n<li><strong>Ask people about what they do<\/strong>. I recommend that you follow\u00a0<a href=\"https:\/\/www.linkedin.com\/in\/mattmayo13\/\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>Matthew Mayo<\/strong><\/a>\u00a0post on \u201cA day in the life of a Data Scientist\u201d to have a better idea of what we do.<\/li>\n<li>If you want an internship, have your\u00a0<strong>academic skills on point.<\/strong><\/li>\n<\/ul>\n<h2 style=\"margin-left: -1.2pt;\"><strong>Creating a Resume and a Life (what?) to get that\u00a0job<\/strong><\/h2>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 600px; height: 501px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*iwyQh4lC1RvS0UqrMavfyA.png\" alt=\"experfy-blog\" \/><\/p>\n<p>Advice from\u00a0<a href=\"https:\/\/medium.com\/@MarkMeloon\" target=\"_blank\" rel=\"noopener noreferrer\">Mark Meloon<\/a>:<\/p>\n<p><em>If you have something you want the reader or listener [interviewer] to know, you\u2019d better put that up front in your message. For resumes, that means you lead with your strongest aspect. Maybe that\u2019s your education. Maybe it\u2019s your job experience.Don\u2019t feel that you have to follow the order in that resume template you downloaded.<\/em><\/p>\n<p><em>When an interviewer asks, \u201cTell me about yourself\u201d, you don\u2019t need to give them a chronological account of your life story. Start by telling them what your #1 strength is.<\/em><\/p>\n<p>More from Mark:<\/p>\n<p><em>You want to communicate your passion for the field? Do some personal projects. Contribute to open source. Start a blog. Heck, be active [\u2026] on LinkedIn.<\/em><\/p>\n<p><em>Words are cheap; actions are what counts.<\/em><\/p>\n<p><em>And in our competitive field, you want to avoid doing anything that will cause people to not take you seriously.<\/em><\/p>\n<p>Advices from\u00a0<a href=\"https:\/\/www.linkedin.com\/in\/kylemckiou\/\" target=\"_blank\" rel=\"noopener noreferrer\">Kyle Mckiou<\/a>:<\/p>\n<p><em>Turn every bullet point on your resume into a mini story. You\u2019ve probably already got a full page of text, and it\u2019s probably cluttered with one-sentence junk that says \u201cI did this\u201d or \u201cwe did that.\u201d\u00a0<\/em><strong><em>Go ahead and delete half of that.<\/em><\/strong><\/p>\n<p><em>Now that you\u2019ve freed up some space, start expanding on the remaining accomplishments.<\/em><\/p>\n<p><em>Use the\u00a0<a href=\"https:\/\/en.wikipedia.org\/wiki\/Situation,_task,_action,_result\" target=\"_blank\" rel=\"noopener noreferrer\">STAR<\/a>\u00a0format to give each bullet point context and to turn it into a detailed mini story with a resolution.<\/em><\/p>\n<p><em>It\u2019s better to have a few standout stories and accomplishment on your resume than a whole lot of \u201cstuff.\u201d<\/em><\/p>\n<p>More from Kyle:<\/p>\n<p><em>[when communicating with the recruiter] boil your communication down to 3\u20135 sentences that explain:<\/em><\/p>\n<p><em>&#8211; Why you\u2019re interested in the job and company<\/em><\/p>\n<p><em>-Why your skills and background make you a good fit.<\/em><\/p>\n<p><em>Also,<\/em><strong><em>\u00a0be excited and passionate.<\/em><\/strong><\/p>\n<p><strong><em>Outwork the competition.<\/em><\/strong><\/p>\n<p>Advices from\u00a0<a href=\"https:\/\/www.linkedin.com\/in\/eric-weber-060397b7\" target=\"_blank\" rel=\"noopener noreferrer\">Eric Weber<\/a>:<\/p>\n<p><em>Want to make an impact as a data scientist? Don\u2019t only look at what is being done, but also what is NOT being done.\u00a0<\/em><strong><em>Write<\/em><\/strong><em>\u00a0out a list of the Top 5 things you could do to help the company. Then pitch your ideas.<\/em><\/p>\n<p><em>Why?<\/em><\/p>\n<p><em>1.\u00a0<\/em><strong><em>It is hard to be self-critical.<\/em><\/strong><em>\u00a0Examining what is not being done is hard but can push you outside the comfort zone of \u201clet\u2019s just keep things running like they are\u201d.<\/em><\/p>\n<p><em>2.<\/em><strong><em>\u00a0Business moves fast<\/em><\/strong><em>. It can be hard to get out of the \u201cget shit done\u201d mentality when things seem to be on fire. But stepping away from that mentality provides a chance to be truly innovative.<\/em><\/p>\n<p><em>3.\u00a0<\/em><strong><em>You know the data really well<\/em><\/strong><em>. Very few others do. Understanding the potential of data is a data science job, not always something that management can always do.<\/em><\/p>\n<p><em>4.<\/em><strong><em>\u00a0Writing out a list forces you to track your thoughts over time.<\/em><\/strong><em>\u00a0You commit it to paper and it will stick with you. In contrast, just thinking about something doesn\u2019t always make it stick.<\/em><\/p>\n<p><em>5.<\/em><strong><em>\u00a0You must sell your ideas<\/em><\/strong><em>. Simply writing them is okay but without you pursuing your ideas to management, they won\u2019t get off the ground. Pick your favorite one and start identifying its impact and ROI for the company.<\/em><\/p>\n<p><strong><em>Thinking, writing, and selling.<\/em><\/strong><em>\u00a0Push yourself to do this regularly and you\u2019ll find all sorts of new ideas to share.<\/em><\/p>\n<p>Advices from\u00a0<a href=\"https:\/\/medium.com\/@beauwalker\" target=\"_blank\" rel=\"noopener noreferrer\">Beau Walker<\/a>:<\/p>\n<p><em>Over the past\u00a0<\/em><strong><em>ten years<\/em><\/strong><em>\u00a0I\u2019ve applied to\u00a0<\/em><strong><em>898<\/em><\/strong><em>\u00a0jobs on LinkedIn. I know this because LinkedIn keeps track. (Thanks for the reminder LinkedIn!)<\/em><\/p>\n<p><em>This number doesn\u2019t include the jobs I\u2019ve applied to on other platforms or directly on employer sites. It also doesn\u2019t include the numerous recruiter emails, InMails, and phone calls I\u2019ve received.<\/em><\/p>\n<p><em>Want to know how many jobs I\u2019ve actually\u00a0<\/em><strong><em>taken<\/em><\/strong><em>\u00a0as a result of these activities?<\/em><\/p>\n<p><strong><em>Zero. Zilch. Cero. \u043d\u0443\u043b\u044c. It\u2019s true.<\/em><\/strong><\/p>\n<p><em>I\u2019m 0\u2013898 for jobs from LinkedIn! And I\u2019ve never actually taken a job that I\u2019ve found through a job board or recruiter.<\/em><\/p>\n<p><em>I get asked a lot about how to find a job. And I talk a lot with people who are discouraged by the application process.<\/em><\/p>\n<p><em>My advice? Consider alternative approaches to finding a job. In the past 10 years, every job I have taken has come from networking. The best jobs often do.<\/em><\/p>\n<p>Advices from\u00a0<a href=\"https:\/\/www.linkedin.com\/in\/vineetvashishta\" target=\"_blank\" rel=\"noopener noreferrer\">Vin Vashinta<\/a>:<\/p>\n<p><em>[\u2026] when you\u2019re in the mode of answering questions, it\u2019s tough to start asking them. When you\u2019re in the mode to impress, it\u2019s tough to expect the same in return. Remember that\u00a0<\/em><strong><em>hiring is a 2-way street.<\/em><\/strong><\/p>\n<p><em>Leave an interview with the team wanting more but also expect to leave the interview with the same desire yourself. Were YOU impressed? What did they do to make YOU feel welcome? You\u2019ve put in work to get to where you are now. Gravitate towards those businesses that lift you up rather than diminish all you\u2019ve achieved.<\/em><\/p>\n<p><em>Great companies put in work to leave every candidate blown away, even the ones they don\u2019t hire. Amazon is an excellent example of a company that has impressed me with their hiring process. I\u2019ve had multiple dealings with their recruiters; always professional, quick to respond, &amp; bringing roles that are good fits for my capabilities.<\/em><\/p>\n<p>Advices from\u00a0<a href=\"https:\/\/medium.com\/@jtkostman\" target=\"_blank\" rel=\"noopener noreferrer\">JT Kostman, PhD<\/a>:<\/p>\n<p><em>The problem most likely has to do with how you think about your resume.<\/em><\/p>\n<p><em>Q: What is the job of the resume?<\/em><\/p>\n<p><em>A: Wrong. It\u2019s not to get you a job<\/em> <em>\u2014<\/em> <em>or even an interview, or to the hiring manager.\u00a0<\/em><strong><em>The job of the resume is to make it past the shredder<\/em><\/strong><em>. Period. Full stop.<\/em><\/p>\n<p style=\"text-align: center;\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/600\/1*JE2wRGD1cTru1fJ3wAVdtQ.gif\" alt=\"experfy-blog\" \/><\/p>\n<p><em>Most [\u2026] peole who get your resume have absolutely no idea what we really do; they just have a list to check. They\u2019re looking for keywords<\/em> <em>\u2014<\/em> <em>not concepts. Most of the them are not going to be bothered with having to pan <\/em><em>for gold. They\u2019re going to give it less than a minute (literally) and move on to the next one on their pile.<\/em><\/p>\n<p><em>Be honest: Is your resume so simple even some Bozo in HR\/Recruiting can see how you would be a near ideal match? And is it about you? Or are you clearly showing (not telling) how you would benefit the hiring manager AND the company<\/em> <em>\u2014<\/em> <em>including ensuring she can take fewer Tums every day? Are you connecting the dots for HR and drawing them a map?<\/em><\/p>\n<p><em>Probably not.<\/em><\/p>\n<div align=\"center\">\n<hr align=\"center\" size=\"0\" width=\"100%\" \/>\n<\/div>\n<p>Read all of that advices, and look for more. They are great. Some a little be hard to read, but they are true.<\/p>\n<p>So what do I have to tell you to improve your life as a DS and also your resume? Here is my list:<\/p>\n<ol>\n<li><strong>Be honest.<\/strong>\u00a0Don\u2019t undersell or oversell your self in your resume.<\/li>\n<li><strong>Connect and be active in the data science community.<\/strong>\u00a0Create blogs, share your knowledge, participate in open source projects.<\/li>\n<li><strong>Be clear.<\/strong>\u00a0Read 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.<\/li>\n<li><strong>Don\u2019t send the same resume to every company.\u00a0<\/strong>This is very close to the last point, and it\u2019s a hard job. But believe me you\u2019ll see results much faster. Analyze the company and create a resume specific for that position.<\/li>\n<li><strong>Keep it short.\u00a0<\/strong>They get thousands of resumes everyday, so they will only expend around 30\u201360 seconds reading yours. So be sure that you are putting there the things they want to see. Don\u2019t put there stuff that is not relevant for the company.<\/li>\n<li><strong>Be consistent<\/strong>. That means same font and style everywhere.<\/li>\n<li><strong>Tell your story.\u00a0<\/strong>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.<\/li>\n<li><strong>Ask the recruiter for advise before sending the resume.<\/strong>:<\/li>\n<\/ol>\n<p style=\"margin-left: -1.2pt;\"><strong>More Advice<\/strong><\/p>\n<p style=\"margin-left: -1.2pt; text-align: center;\">\n<h2 style=\"margin-left: -1.2pt;\"><strong>General interview tips<\/strong><\/h2>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 600px; height: 428px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*af2eeWC-03LEIVKV6wC-qg.jpeg\" alt=\"experfy-blog\" \/><\/p>\n<p>Before going to some advices for the interview, here\u2019s a list of a \u201ccommon\u201d process when applying for a Data Science position:<\/p>\n<ol>\n<li>A phone call where they will ask you about you and your experience. This is the first phone screen.<\/li>\n<li>If everything goes well you\u2019ll 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.<\/li>\n<li>(Optional) Data science task. They\u2019ll 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.<\/li>\n<\/ol>\n<div align=\"center\">\n<hr align=\"center\" size=\"0\" width=\"100%\" \/>\n<\/div>\n<p>Advices from\u00a0<a href=\"https:\/\/www.linkedin.com\/in\/kylemckiou\/\" target=\"_blank\" rel=\"noopener noreferrer\">Kyle Mckiou<\/a>\u00a0to write good quality code as a Data Scientist:<\/p>\n<p><em>Writing quality code is critical for data scientists, especially in 2018 and beyond.<\/em><\/p>\n<p><em>As data science practices mature, more companies will demand automation, reproducibility, scalability, portablitiy, and extensibility for data science projects. To make this a reality, you had better be ready to write quality code.<\/em><\/p>\n<p><em>Here are 10 tips to get you started as a data scientist:<\/em><\/p>\n<p><em>1. Refactor aggressively. Don\u2019t tack new code onto the end of a script when you should refactor<\/em><\/p>\n<p><em>2. Follow style standards, e.g. PEP-8 for Python<\/em><\/p>\n<p><em>3. Code defensively<\/em> <em>\u2014<\/em> <em>always think about what could go wrong!<\/em><\/p>\n<p><em>4. Avoid globals and minimize the scope of variables<\/em><\/p>\n<p><em>5. Take those scripts and turn them into programs<\/em> <em>\u2014<\/em> <em>create organized systems<\/em><\/p>\n<p><em>6. Always unit and integration test<\/em><\/p>\n<p><em>7. Automate your tests<\/em><\/p>\n<p><em>8. Create a rigorous review process<\/em><\/p>\n<p><em>9. Follow the rigorous review process, even when you don\u2019t want to!<\/em><\/p>\n<p><em>10. Provide honest, critical feedback as a reviewer and be open to feedback when your code is being reviewed<\/em><\/p>\n<div align=\"center\">\n<hr align=\"center\" size=\"0\" width=\"100%\" \/>\n<\/div>\n<p>4. Whiteboard programming. This maybe the harderst and more intimidating part of any process. Programming in a blank space. Just you and a piece of paper. Practice this a lot. You don\u2019t need to write the code here perfectly, they want to see you thinking and getting into the solution. Talk and describe your thinking process, don\u2019t be there quite.<\/p>\n<p>5. (Optional) Day of coding in the company. This is the final task, is not that common, but is an invite for their company to be there for a full day, seeing what they do and solving some programming tasks.<\/p>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 600px; height: 375px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*8_deSUMrLjYji_p4wN2NpA.jpeg\" alt=\"experfy-blog\" \/><\/p>\n<p>Advices for the interview? Here are the ones I could find, they are great:<\/p>\n<p><a href=\"https:\/\/medium.com\/@brohrer\" target=\"_blank\" rel=\"noopener noreferrer\">Brandon Rohrer<\/a>:\u00a0<a href=\"https:\/\/brohrer.github.io\/how_to_interview.html\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/brohrer.github.io\/how_to_interview.html<\/a>\u00a0(READ THIS!!!)<\/p>\n<h2 style=\"margin-left: -1.6pt; text-align: center;\"><\/h2>\n<h2 style=\"margin-left: -1.6pt;\"><strong>The Recruiter side<\/strong><\/h2>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 600px; height: 261px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*CstIq7j6OiVrbWQNnhR9xw.jpeg\" alt=\"experfy-blog\" \/><\/p>\n<p>If you are a recruiter for Data Science positions, first see whom is Data Scientist. Not an easy question but here\u2019s my short answer to that:<\/p>\n<p><em>A Data Scientist is a person in charge of\u00a0<\/em><strong><em>analyzing business problems<\/em><\/strong><em>\u00a0and give a\u00a0<\/em><strong><em>structured solution<\/em><\/strong><em>\u00a0starting by\u00a0<\/em><strong><em>converting<\/em><\/strong><em>\u00a0this\u00a0<\/em><strong><em>problem<\/em><\/strong><em>\u00a0into a\u00a0<\/em><strong><em>valid\u00a0<\/em><\/strong><em>and\u00a0<\/em><strong><em>complete question<\/em><\/strong><em>\u00a0, then using\u00a0<\/em><strong><em>programming<\/em><\/strong><em>\u00a0and\u00a0<\/em><strong><em>computational tools<\/em><\/strong><em>develop\u00a0<\/em><strong><em>codes<\/em><\/strong><em>\u00a0that\u00a0<\/em><strong><em>clean, prepare and analyze\u00a0<\/em><\/strong><em>the\u00a0<\/em><strong><em>data<\/em><\/strong><em>\u00a0to then create\u00a0<\/em><strong><em>models<\/em><\/strong><em>and\u00a0<\/em><strong><em>answer<\/em><\/strong><em>\u00a0the initial question.<\/em><\/p>\n<p>What data science is not:<\/p>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 600px; height: 569px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*vHbv4T7URmmDpFx0yZKlPw.png\" alt=\"experfy-blog\" \/><\/p>\n<p style=\"text-align: center;\">We are much more than\u00a0this.<\/p>\n<h2 style=\"margin-left: -1.2pt;\"><strong>Why is Data Science important?<\/strong><\/h2>\n<p><em>Data Science and Analytics exists because hidden in the data there are treasures waiting to be discovered.<\/em><\/p>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 600px; height: 203px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*4kmmCzCjY_AWBzbGlkGWHA.png\" alt=\"experfy-blog\" \/><\/p>\n<p><strong>The Ways a Data Scientist Can Add Value to Business:<\/strong><\/p>\n<p style=\"text-align: center;\">\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 600px; height: 454px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*_F1RX4mQHa7jM3_fFt8vwg.png\" alt=\"experfy-blog\" \/><\/p>\n<p style=\"text-align: center;\">\u00a0\u00a0From simplilearn.<\/p>\n<p>1. Empowering management and officers to make better decisions<\/p>\n<p>2. Directing the actions based on trends which in turn help in defining goals<\/p>\n<p>3. Challenging the staff to adopt best practices and focus on issues that matter.<\/p>\n<p>4. Identifying opportunities<\/p>\n<p>5. Decision making with quantifiable, data-driven evidence.<\/p>\n<p>6. Testing these decisions<\/p>\n<p>7. Identification and refining of target audience<\/p>\n<p>8. Recruiting the right talent for the organization<\/p>\n<h2 style=\"margin-left: -1.2pt;\"><strong>Do you always need a Data Scientist?<\/strong><\/h2>\n<p>Actually no. I recommend that you read these articles on the subject,<\/p>\n<p style=\"text-align: center;\">\n<p>From those, an important quote I can take is:<\/p>\n<p><em>\u2026 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.<\/em><\/p>\n<p><em>Without these elements in place,\u00a0<\/em><strong><em>you\u2019ll have a sports car with no fuel<\/em><\/strong><em>. Ask yourself if more traditional roles like data analysts and business intelligence may suffice.<\/em><\/p>\n<p>Remember this words: A bad data scientist is way worse than don\u2019t have a data scientist at all.<\/p>\n<p>There\u2019s lot of people wanting a job in Data Science, most of them are really intelligent people, wanting to help and have a path in this area, but be careful before hiring one. I recommend that you search for data science descriptions in the best companies out there, learn about their process, and learn from them.<\/p>\n<p>Also, is not true that they need a PhD to be the best data scientists. They need experience working with data and solving business questions using data science. Before asking for a PhD, ask for knowledge, projects they have worked on, open source projects they built or collaborate, Kaggle kernels they created, related job experience, how did they solve an specific problem.<\/p>\n<p>Data science is not just an IT area, is IT+Business, you need to be sure that the data scientist you hire can adapt to the company, understand the business, have meetings with stakeholders and present their findings in a creative and simple way.<\/p>\n<p>Read this blog post for more information:<\/p>\n<p style=\"text-align: center;\">\n<p>and from there some important tips to recruit data scientists:<\/p>\n<ol>\n<li>Recruiters, work closely with hiring managers to build out\u00a0<strong>accurate job descriptions.<\/strong><\/li>\n<li>Iron out nuances to\u00a0<strong>distinguish\u00a0<\/strong>which<strong>\u00a0types of data scientists\u00a0<\/strong>will be the\u00a0<strong>best fit for the business<\/strong>\u2019 needs. Hone in on the skillset and experience of the type of data scientist you\u2019re looking for.<\/li>\n<li><strong>Think long term<\/strong>. Understand how the org plans to leverage this role within the product roadmap.<\/li>\n<li><strong>Set realistic expectations<\/strong>\u00a0of available candidate pool. There are more roles than candidates, so recruit accordingly.<\/li>\n<li>Build a list of ideal candidates and calibrate with hiring manager to\u00a0<strong>gauge fit against reality of talent market.<\/strong><\/li>\n<\/ol>\n<p>A good quote on the recruiting process from Vin Vashinta:<\/p>\n<p><em>Aspiring data scientists want 1 thing from the companies that don\u2019t hire them: an explanation. In many cases their only response is silence. How\u2019s an aspiring data scientist supposed to know what to work on, if companies won\u2019t tell them?<\/em><\/p>\n<p><em>Aspiring data scientists aren\u2019t psychics, but they are hardworking &amp; willing to learn. They\u2019ll rise to the challenge if companies start telling them where the bar is.<\/em><\/p>\n<p><em>Peel back the hiring process at most companies &amp; you\u2019ll find they can\u2019t objectively answer the question, \u201cWhy didn\u2019t you interview or hire this person?\u201d I teach clients how much they can learn by examining the candidates they reject as closely as they examine the people they hire.<\/em><\/p>\n<p><em>There\u2019s value to both candidates &amp; 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.<\/em><\/p>\n<p><em>Beyond the value, it\u2019s the decent thing to do for someone who took the time to apply. Hiring is all about making connections. Silence shows the company doesn\u2019t care enough to treat people the right way. That\u2019s something candidates remember.<\/em><\/p>\n<div align=\"center\">\n<hr align=\"center\" size=\"0\" width=\"100%\" \/>\n<\/div>\n<p>I hope this post will help everyone in the Data Science world. Let\u2019s join together and help each other transform the world into a better place. Remember to have fun and that there\u2019s much more in life than work, I love what I do, but take time for your family and friends, be happy and be kind to one another.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Ready to learn Data Science? Browse courses\u00a0like\u00a0Data Science Training and Certification developed by industry thought leaders and Experfy in Harvard Innovation Lab. Are you a Data Scientist looking for a Job? Are you a Recruiter looking for a Data Scientist? If you answered yes or NO to this questions you need to read\u00a0this. Hello! This<\/p>\n","protected":false},"author":252,"featured_media":3006,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[94],"ppma_author":[2881],"class_list":["post-1322","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-data-science"],"authors":[{"term_id":2881,"user_id":252,"is_guest":0,"slug":"favio-vazquez","display_name":"Favio V\u00e1zquez","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"V\u00e1zquez","first_name":"Favio","job_title":"","description":"<a href=\"https:\/\/towardsdatascience.com\/@favio.vazquezp?source=post_header_lockup\">Favio V&aacute;zquez<\/a>, physicist and computer engineer, is Data Scientist at <a href=\"http:\/\/www.bbvadata.com\/\">BBVA Data &amp; Analytics<\/a>. He works on Big Data, Data Science, Machine Learning and Computational Cosmology. Since 2015, he&#039;s been part of the Apache Spark collaboration, with some minor bug fixes, and improvement of documentation."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1322","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/252"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=1322"}],"version-history":[{"count":3,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1322\/revisions"}],"predecessor-version":[{"id":30089,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1322\/revisions\/30089"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3006"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=1322"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=1322"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=1322"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=1322"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}