Is it OK To Allow Data Scientists / IT Engineers to Work from Home


Operating a company or having a business needs some technical expertise that you cannot otherwise seek around.  The technological talents and skills are sometimes hard to find or at some point need to be done when hiring cycles would not help fix your problem.  Through technology, work can be done remotely from anywhere, work from home, Virtual Assistant (VA) jobs, road warriors and mobile workers or telecommuters are a few to name.  With the equal opportunity of flattening the world through technological advancement on communications and collaboration these can help your business or company to help you and support you, also minimizing overhead on office space costs.

Selecting the best talent is the best guide to make your search for the remote worker or remote expert that you are looking.  Consultants on these field are mostly checked by their resume and output of their work portfolios so you know what quality of work you can expect.  Remote working really helped most businesses including online marketers and others.  Now, even works on appointment setting, writing, graphics design, social media management, mail, booking, telemarketing, programming or development, systems engineering, systems administration or DevOps can be done remotely.

Through the trend of needs for analytics talents, Data Science field have a lot of scientists out there that are also ready to be tapped, they are just around, but mostly are in a remote areas.  It is hard to find experts like Data Scientist, Analysts, and IT / Systems Engineers, but if you would look around, you can even find them online.  The quality of work can also be good, and if you want them to be better for helping you more, let them take courses, pay for their education, since online courses made quality education reachable as long as you are connected to the internet and submit your work online.

Allow knowledge workers to work online, they give them the flexibility, cognitive ability and quality time and freedom, which makes it good to let Data Scientist / Engineers remotely or let them work from home.

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Finding the Unicorn Data Scientist

Nowadays, as more shifters or career shifters are around there are people who declared themselves as Data Scientist.  These abundance are somewhat we can call as raw skills.  They may possess skills at some level but it is not yet enough.  In order for us to beat the uncertainty, let us see what it takes to be a data scientist. In order to be a Data Scientist, there are 3 major skills, the Computer Science / Hacking skills, Mathematics & Statistics skills and Substantive knowledge like research, business, medicine, biostatistics and other domain skills and knowledge. Machine learning also come into play which is part of Artificial Intelligence, it is where we model the world in order to be able to forecast and predict information.  Knowledge of programming, especially functional programming, database systems and big data is necessary for the technical skills. Statistics and Mathematics skills and knowledge are also needed in order to be a Data Scientist to have the expertise on looking and solving problems and expressing them in figures and numbers.  With these the data scientist can model the world in numbers or they are able to quantify the world that we are living in in order to infer analysis. Substantive knowledge on the other hand will come into skills with research, business, biostatistics or medicine and other fields which may need some knowledge of the problem being observed and analyzed through the use of data. Other skills needed for Data Scientist is the ability to present his work to non-technical people and visualize the findings so as to make the relay of information in simpler forms so that everyone can get a grasp of the information being communicated. Unicorn Data Scientists are those that have accidentally gained different skills.  In my case, I am a certified IT engineer, have been skilled in Marketing and Digital Marketing, took up Master’s Degree in Business Administration (MBA), been doing Solutions Architecture (Cloud) and other sort of researches like in the biotechnology research on BioPhotonics, bioinformatics and more.  A vast array of experience and exposure to different fields and being able to integrate or having a look at different stages of the industry, business, medical field, sciences, arts and technology is needed in order to be a Unicorn Data Scientist. It is somewhat hard to find them, and most them are either Master’s Degree or Doctor’s Degree (or PhD) holders.  They have a mixed nut in marketing and economics too, and have been around in Research and Development projects, some have experienced multi-shifted career experience, and others are life-long learners that have immersed oneself in Computer Science, Math & Stats and Substantive Knowledge. Unicorn Data Scientists are products of experience, sometimes not of a well lined career path but a path wherein these people have skills in mixed and different fields.  Most of them are fast learners and have even looked into Systems or processes which is one of the applications of data science.  Even in business processes not just data can we apply data science and engineering. If you are lucky to find a Unicorn Data Scientist, be sure to take care of them as they are mostly rare and hard to find.

ScienceOps or SciOps Tasks

Helping other businesses grow in terms of technology is mostly a need, in some start-ups and also to the growing business that wants business continuity.  Technology nowadays makes creation of services delivered at a faster rate due to the services that we have like Cloud services.  Amazon Web Services, OpenStack and many more have offered their infrastructure as a service for those technology business to help deliver high quality applications.

Users of the apps are now mobile on their laptops, they are now on smart phones, laptops, tablets, and many other gadgets ready to run apps.  Also, Apple devices are around and all those user apps are best paired with data on some server, the cloud.  The cloud works as an authentication, knowledge-base and even the data storage of these infrastructure.  Regardless of the application a good system and network infrastructure is best to have.

These infrastructures are built on top of on-premise computers/servers and even with the cloud.  Cloud services reduces costs is such a way that you don’t have to spend time waiting for arrival of servers, you can scale up and down and you can even scale automatically, yes automatic scaling.  But what is automatic scaling?  Your infra if properly managed can be designed to grow its instances/servers when there is a high need of resources and to shrink down in numbers when only few instances are needed.  All these are beneficial.

With all of these things, we have infra security.  Securing, maintaining and keeping up-to-date with the latest software, fixes and patches are needed on your cloud infra.  However, we are not sure where we are doing right or where we are missing if don’t audit.

Designing a good secured infra needs some more.  We have to monitor things in order to know downtimes, also we have to measure.  In measuring the infra performance we create metrics that checks the status or measurement of every parameters for the server.  We then have to collect this and increase the capability to analyze it.

The data scientist for this work are ScienceOps. They are mostly building good infrastructure, but that is not all.  Even after seeing what’s good in your infra, you will need to know what’s in for your business.  ScienceOps might need some background in business, marketing and strategic planning, and also the apps that you are targeting for measurements.  SciOps will then need to create analysis of your users data aside from the server logs.  With the users data, they can show you how you are fairing with the market.  They can show you if you are growing over time or just having stagnant users no longer interacting with your app.

Your marketing success can be measured and a lot more on the choices of products, services and behavior of your customers or users of your app.  With proper metrics, right analysis and more insight gains, the management team and product team can design, update and direct the product / service that you have in the right direction that satisfies your customers and service users.

Machine Learning, A Look in the Past

Before the Big Data become popular, there were at the back of Web 1.0 the machine learning of the past which utilizes Market Basket Analysis. These are very dominant in advanced e-commerce stores and online shops. The Job sites also utilized these technology before, and how did they implement it? Cookies, not those in your kitchen jar, but those text files that remembers your preferences, your visited sites and the things that you’ve clicked on the internet.

And what was that? Machine Learning, a part of the task of a so-called Data Scientists of today. Facebook analyzes all of our likes, shares, streams today, Twitter can also do it, I have even tried to do sentiment analysis of tweets using python. Google with their intelligent algorithms, Yahoo the early adopter of Hadoop for HDFS (a Big Data System). A lot of other database management systems like SQL are there used widespread. In those days, MatLab is a mostly used software, SPSS, SAS, S-Plus, and now R. Nowadays there is Pig to simplify MapReduce, the language for Hadoop management.

But who are those that have benefit from data science in the past? Amazon, the online book store have utilized data science, data mining, data analysis in order to show you the most relevant product that you can buy, they are now an online store and have even adopted into Cloud Service Provider company. Their algorithms can help upsell and show you related items to what you have already bought.

The most successful in utilizing BIg Data and Data Science is Walmart, they know how much to display on store, they know how much to carry on their inventory and they even know when you will buy your next coffee beans, sugar and even the infant milk and cereals that you consume and buy on your scheduled shopping. The likes of forecasting sales, that is why Walmart grew because of this so called business intelligence, it is data science, they use algorithms, mathematical equations, operations research tools in order to manage and understand the consumer behavior.

So the realization of Data Scientists today are thing of the past, but now, a successful e-scientist must have the skills in diverse fields (multidisciplinary-skilled) like business / marketing, economics, mathematics, statistics, operations research, some IT skills, big data and creativity. Yes, creativity, without it there will be no spark of wisdom, and this is mostly part intuition, insight and looking the world/data at a different angle to predict, to deduce and to induce.