Jobseekers often ask what does IBM look for when hiring a data scientist. Member from IBM analytics team say that it is extremely important to differentiate a true data scientist from professionals in other similar data job roles like data engineers, AI application developers and business analysts. IBM is looking for professionals who can run and complete data science projects and highlights the below skills of utmost importance for a data scientist -
(Source : https://www.techgig.com/tech-news/this-is-what-ibm-looks-for-in-a-data-scientist-146010 )
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For two subsequent years, Data Scientist has been crowned as the best job in America because of the salary , number of job openings and satisfactory rating. National Academies of Sciences, Engineering and Medicine has defined the important aspects universities must consider when developing a data science training program. A good data science education program should have 3 important aspects -
i) Students should develop a data acumen or must acquire the ability to know what they should do with the data. Thus, the data science course curriculum developed should take into consideration the entire life cycle of an actual data scientist.
ii) Any data science training program should have the students work with real datasets so that they do not face any difficulties while applying the data science concepts learned in the classroom to their career.
iii) The data science education should educate the students on data governance and ethic ranks
(Source : https://edtechmagazine.com/higher/article/2017/12/3-aspects-good-data-science-education )
Hiring a data scientist plays a vital role at Intel as the company moves from a PC-centric to data-centric company. At a Harvard University talk , Bob Rogers, Intel’s chief data scientist highlights the key traits he looks for when hiring data scientists -
(Source : http://searchcio.techtarget.com/blog/TotalCIO/What-Intels-Bob-Rogers-looks-for-when-hiring-data-scientists )
BMO (Bank of Montreal) based in Toronto is one of the top 5 Canadian banks that plans to implement cognitive technologies.BMO has a “SmartCore” of data and services capabilities that will facilitate the implementation of future analytics , data science, and any other cognitive activity in real-time. The SmartCore contains provisioning of data records, metadata hub, and reference data. The “smart” components of the core include a data science platform that has analytics sandboxes and open sources softwares for machine learning and other softwares for robotic process automation.Traditional analytics at BMO relied on data structures that did not change often , for instance capital model allocation or reporting. BMO now plans to broaden its analytics capabilities to support emerging customer experience and business requirements like credit underwriting and product recommendations. As a result of which BMO plans to make some organizational restructuring for data science and cognitive technologies. As data science experts become a vital component of bank’s future , they plan to have another central coordination of their activity.
(Source : https://www.forbes.com/sites/tomdavenport/2017/12/08/setting-the-table-for-data-science-and-ai-at-bank-of-montreal/#1344dbe07814 )
Machine learning engineers, data scientists and big data engineers rank among the top emerging jobs on LinkedIn as organizations across various industries seek to hire them.The job role of a data scientist has grown over 650% since 2012. US has only 35,000 professionals with data science skill set , whilst there are 100’s of organizations hiring for these roles. According to LinkedIn’s 2017 U.S. Emerging Jobs Report, there are 5.5. Times more big data developers than that existed 5 years ago and 6.5 times more data scientists. 2018 is the best time to hone data science skills and hit up on the most rewarding career.
(Source : https://www.forbes.com/sites/louiscolumbus/2017/12/11/linkedins-fastest-growing-jobs-today-are-in-data-science-machine-learning/#117a379d51bd )
A research conducted by Educational Career website 365 Data Science on 1001 data scientist profiles on LinkedIn to decipher the characteristics required to land a data scientist job reveals interesting must have skills for a data scientist.According to the research , to succeed as a data scientist , one needs to have expertise in Python , R or SQL. 53% of data scientist profiles surveyed work either with Python or R programming languages while SQL seemed popular among data scientists in UK. On the academic front , 40% of the data scientists have taken field related online data science courses, with an average of 3.33 data science certifications on each LinkedIn resume.
(Source : https://www.computerworlduk.com/careers/research-reveals-skills-needed-find-work-as-data-scientist-3668831/)
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Data and algorithms have become the key to success for the fast growing apparel subscription service Stitch Fix. Stitch Fix gathers data about its customers on what they are looking for so that stylists can provide right recommendations to their customers. Each customer directly provides 85 meaningful data points through their style profile that include detailed size, fit, price preferences, style and other data on how often they dress for particular occasions or what part of the body do they like to cover or flaunt. Stitch Fix has over 75 data scientists who plug all that information to the algorithms which then make suggestions on what products to offer in each order and that comes out with the probability that a given item will match a particular customer. Having got the probability it is fed through a human stylist that provides the finishing touches before finalizing on a order for a customer.
With precision medicine efforts going mainstream in the healthcare sector , increasing number of health systems are working hard to get their hands on large amounts of clinical data. Michael Draugelis, data scientist at penn Medicine says that AI and Machine Learning has huge potential to drive analytics on large volumes of clinical data ,however care should be taken to enable consistent and repeatable performance. He further mentions that getting consistent repeatable performance the data progresses in the clinical setting is not at all easy but there are various ways of managing people, process and technology to improve the odds. Driving innovation using AI is a learning curve in the healthcare sector and Draugelis has offered some great real-world insights in his interview with HealthCareITNews that he has derived from his Penn Medicine projects.
(Source : http://www.healthcareitnews.com/news/penn-medicine-data-scientist-gives-lessons-applying-ai-precision-medicine )
First, a startup from Durham, NC is leveraging the power of data science and machine learning to help real estate agents. They have built a SaaS platform that analyses 700 personal factors (including income changes, purchase behavior, various life events , demographics and more )of every real estate agent’s contact and cross references them with national averages. The platform generates a seller score that indicates the likelihood of a person selling their house which helps real estate agents know exactly when they should reach out to a customer for landing a deal. The SaaS platform tracks attributes and home-selling behavior of 214 million people in US to calculate seller scores that get updated automatically when any of the factors change. This seller score serves as a guide for outreach and help agents win more deals in less time.
(Source : //www.forbes.com/sites/matthunckler/2017/12/20/this-real-estate-startup-uses-data-science-to-predict-home-listings-before-they-happen/#1b76493838d8 )
A McKinsey report estimates that there will be a shortage of 290,000 data scientists in US by 2018. Big data powered with Machine Learning and AI will be the next natural resource just like air, oil and water making it a multi-trillion dollar opportunity. Forrester estimates that the cognitive era will create 8.9 million new jobs in US by 2025 in data science, automation, content and robot monitoring. Techies need to upskill or reskill themselves to be ahead of the curve and fit into the new hybrid workforce and work in coordination with machines.
(Source : https://www.rtinsights.com/for-data-scientists-in-an-ai-world-is-it-survival-of-the-fittest/)