Between LinkedIn, Glassdoor, and Indeed there are over 12,000 open remote-based software technical professional jobs available today. Companies with open positions on these sites include Aha!, AutoDesk, Calix, Cardinal Financial, Chef Software, CoreLogic, Couchbase, Medidata Solutions, OutSystems, Qlik, RedHat, Riverbend, Salesforce, ServiceNow, Ultimate Software and many others.
Working Nomads has 10,127 open remote-based developer jobs listed, along with over 1,800 in tech marketing, 1,700 in management, and just over 1,000 in tech sales.
These and many other insights are from a comparison of the leading tech companies who offer remote, work-from-home job positions today and their Glassdoor scores. Tech companies with open remote positions are compared on two Glassdoor scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO. Of the many companies include in the comparison, PowerToFly and GitLab stand out as exceptional in their ability to create strong virtual organizations. They’re defining the future of work today.
PowerToFly was launched by Milena Berry and Katharine Zaleski in 2014 to connect Fortune 500 companies and fast-growing startups with women who are looking to work for companies that value gender diversity and inclusion. PowerToFly is building the platform to propel diversity recruiting and hiring. PowerToFly’s job search engine currently has 994 open remote positions. Founders Milena Berry and Katharine Zaleski have created an excellent remote-based culture where 95% of employees would recommend working there to a friend, and 94% approve of them as CEOs.
FlexJobs has 4,421 remote-based software jobs that include full-time, part-time, and freelance positions. Open positions include Senior Software Engineers, DevOps Engineers, Product Managers, Project Managers, Full Stack Developers, and more.
AngelList has over 18,000 software-related jobs available, including 8,900 open remote-based software positions and 346 open remote product manager jobs today.
FindBacon –Job search site that currently has over 1,300 open positions for developers, technical professionals, including web designers.
GitLab – 100% remote-based company with over 100 open positions as of March 2020.
HackerX – Globally-based job search engine that includes remote-based open positions across Europe and the U.S. There are open positions for Front End Developers, Full Stack Developers, Product Managers, UX Designers, and more.
Jobhunt.ai – Site provides a list of remote-based machine learning jobs currently available.
NODESK – search engine for remote jobs with 592 open positions as of March 22. 2020.
SkipTheDrive – Excellent job search engine comprised of entirely of remote and work-from-home jobs.
Stack Chasers – A Job board for developers made by developers, the site has open positions in the U.S., UK and in global locations available today. Current open positions show a strong focus on DevOps, software engineering, full-stack and front-end development positions.
We Work Remotely site has a broad base of open technical, marketing and sales positions open.
Working Nomads site currently has 10,127 remote, work-from-home development jobs advertised. There are also 1,839 marketing, 1,760 management, 1,658 system administration, 1,469 design and 1,062 sales remote, work-from-home job postings today.
The most common request from this blogs’ readers is how to further their careers in analytics, cloud computing, data science, and machine learning. I’ve invited Alyssa Columbus, a Data Scientist at Pacific Life, to share her insights and lessons learned on breaking into the field of data science and launching a career there. The following guest post is authored by her.
Many people are looking to break into data science, from undergraduates to career changers, have asked me how I’ve attained my current data science position at Pacific Life. I’ve referred them to many different resources, including discussions I’ve had on the Dataquest.ioblog and the Scatter Podcast. In the interest of providing job seekers with a comprehensive view of what I’ve learned that works, I’ve put together the five most valuable lessons learned. I’ve written this article to make your data science job hunt easier and as efficient as possible.
Continuously build your statistical literacy and programming skills. Currently, there are 24,697 open Data Scientist positions on LinkedIn in the United States alone. Using data mining techniques to analyze all open positions in the U.S., the following list of the top 10 data science skills was created today. As of April 14, the top 3 most common skills requested in LinkedIn data scientist job postings are Python, R, and SQL, closely followed by Jupyter Notebooks, Unix Shell/Awk, AWS, and Tensorflow. The following graphic provides a prioritized list of the most in-demand data science skills mentioned in LinkedIn job postings today. Please click on the graphic to expand for easier viewing.
Hands-on training is the best way to develop and continually improve statistical and programming skills, especially with the languages and technologies LinkedIn’s job postings prioritize. Getting your hands dirty with a dataset is often much better than reading through abstract concepts and not applying what you’ve learned to real problems. Your applied experience is just as important as your academic experience, and taking statistics, and computer science classes help to translate theoretical concepts into practical results. The toughest thing to learn (and also to teach) about statistical analysis is the intuition for what the big questions to ask of your dataset are. Statistical literacy, or “how” to find the answers to your questions, come with education and practice. Strengthening your intellectual curiosity or insight into asking the right questions comes through experience.
Continually be creating your own, unique portfolio of analytics and machine learning projects. Having a good portfolio is essential to be hired as a data scientist, especially if you don’t come from a quantitative background or have experience in data science before. Think of your portfolio as proof to potential employers that you are capable of excelling in the role of a data scientist with both the passion and skills to do the job. When building your data science portfolio, select and complete projects that qualify you for the data science jobs, you’re the most interested in. Use your portfolio to promote your strengths and innate abilities by sharing projects you’ve completed on your own. Some skills I’d recommend you highlight in your portfolio include:
Your programming language of choice (e.g., Python, R, Julia, etc.).
The ability to interact with databases (e.g., your ability to use SQL).
Visualization of data (static or interactive).
Storytelling with data. This is a critical skill. In essence, can someone with no background in whatever area your project is in look at your project and gain some new understandings from it?
Deployment of an application or API. This can be done with small sample projects (e.g., a REST API for an ML model you trained or a nice Tableau or R Shiny dashboard).
Julia Silge and Amber Thomas both have excellent examples of portfolios that you can be inspired by. Julia’s portfolio is shown below.
Get (or git!) yourself a website. If you want to stand out, along with a portfolio, create and continually build a strong online presence in the form of a website. Be sure to create and continually add to your GitHub and Kaggle profiles to showcase your passion and proficiency in data science. Making your website with GitHub Pages creates a profile for you at the same time, and best of all it’s free to do. A strong online presence will not only help you in applying for jobs, but organizations may also reach out to you with freelance projects, interviews, and other opportunities.
Be confident in your skills and apply for any job you’re interested in, starting with opportunities available in your network. If you don’t meet all of a job’s requirements, apply anyway. You don’t have to know every skill (e.g., programming languages) on a job description, especially if there are more than ten listed. If you’re a great fit for the main requirements of the job’s description, you need to apply. A good general rule is that if you have at least half of the skills requested on a job posting, go for it. When you’re hunting for jobs, it may be tempting to look for work on company websites or tech-specific job boards. I’ve found, as have many others, that these are among the least helpful ways to find work. Instead, contact recruiters specializing in data science and build up your network to break into the field. I recommend looking for a data science job via the following sources, with the most time devoted to recruiters and your network:
Alyssa Columbus is a Data Scientist at Pacific Life and member of the Spring 2018 class of NASA Datanauts. Previously, she was a computational statistics and machine learning researcher at the UC Irvine Department of Epidemiology and has built robust predictive models and applications for a diverse set of industries spanning retail to biologics. Alyssa holds a degree in Applied and Computational Mathematics from the University of California, Irvine and is a member of Phi Beta Kappa. She is a strong proponent of reproducible methods, open source technologies, and diversity in analytics and is the founder of R-Ladies Irvine. You can reach her at her website: alyssacolumbus.com.
Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn.
Data scientist roles have grown over 650% since 2012, but currently, 35,000 people in the US have data science skills, while hundreds of companies are hiring for those roles.
There are currently 1,829 open Machine Learning Engineering positions on LinkedIn.
Job growth in the next decade is expected to outstrip growth during the previous decade, creating 11.5M jobs by 2026, according to the U.S. Bureau of Labor Statistics.
These and many other insights are from the recently released LinkedIn 2017 U.S. Emerging Jobs Report. LinkedIn has provided an overview of the methodology in their post, The Fastest-Growing Jobs in the U.S. Based on LinkedIn Data. “Emerging jobs” refers to the job titles that saw the largest growth in frequency over that five year period. LinkedIn reports that based on their analysis, the job market in the U.S. is brimming right now with fresh and exciting opportunities for professionals in a range of emerging roles.
Key takeaways from the study include the following:
There are 9.8 times more Machine Learning Engineers working today than five years ago based on LinkedIn’s research, with 1,829 open positions listed on the site today. There are 6.5 times more Data Scientists than five years ago, and 5.5 times more Big Data Developers. The following graphic illustrates the rapid growth of key data scient, machine leanring, big data and full stack developers in addition to sales development and customer success managers.
Software engineering is a common starting point for professionals who are in the top five fasting growing jobs today. The career path to Machine Learning Engineer and Big Data Developer begins with a solid software engineering background. The top five highest growth job typical career paths are shown below:
The skills most strongly represented across the 20 fastest growing jobs include management, sales, communication, and marketing. Additional skills represented across the highest growing jobs include marketing expertise (analytics and marketing automation), start-ups, Python, software development, analytics, cloud computing and knowledge of retail systems.
LinkedIn interviewed 1,200 hiring managers to determine which soft skills are most in-demand and adaptability came out on top. Additional soft skills include culture fit, collaboration, leadership, growth potential, and prioritization.
O’Reilly Media’s Strata, Making Data Work Conference held February 1rst – 3rd, 2011 in Santa Clara, California was one of the most interesting and multifaceted events of the year. Included were presentations on data science, real-time data processing and analytics, data acquisition and crowdsourcing, visualization, in addition to many other topics. You can find the complete list of speaker slides and videos for the event at this link, Strata 2011 Speaker Slides & Videos.
What enriches this conference is the quality of the case studies presented. Be sure to check out the presentation from DJ Patil of LinkedIn on Innovating Data Teams. His discussion illustrates just how critical big data is to LinkedIn and how their approach to managing it enriches the user experience, and is transforming LinkedIn functionality at the same time.
One of the best overall presentations features Dr. Werner Vogels, CTO of Amazon.com titled Data Without Limits. The video is provided below and provides a glimpse into how pervasive AWS is becoming as a foundation for accessing, aggregating and transforming data in real time.