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DATA STRATEGY
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A startup in digital health trained a risk model to open up a robust, precise, and scalable processing pipeline so providers could move faster, and patients could move with confidence after spinal surgery.
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PREDICTIVE ANALYTICS
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Thwart errors, relieve in-take form exhaustion, and build a more accurate data picture for patients in chronic pain? Those who prefer the natural albeit comprehensive path to health and wellness said: sign me up.
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MACHINE VISION
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Using a dynamic machine vision solution for detecting plaques in the carotid artery and providing care teams with rapid answers, saves lives with early disease detection and monitoring.
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INTELLIGENT AUTOMATION
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This global law firm needed to be fast, adaptive, and provide unrivaled client service under pressure, intelligent automation did just that plus it made time for what matters most: meaningful human interactions.
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Mushrooms, Goats, and Machine Learning: What do they all have in common? You may never know unless you get started exploring the fundamentals of Machine Learning with Dr. Tim Oates, Synaptiq's Chief Data Scientist. You can read and visualize his new book in Python, tinker with inputs, and practice machine learning techniques for free. |
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By: Erskine Williams 1 Jan 21, 2022 3:49:26 PM
By Erskine Williams, VP of Delivery at Synaptiq
Congratulations! You’ve just completed PyImageSearch University’s program and now you’re ready to launch your career in Computer Vision. But wait – you don’t have any experience applying these techniques in the real world – how will you get hired? Never fear, there are ways to apply what you know to build up your resume and land your first Computer Vision gig.
First thing’s first: document what you have already done. If you did a research project for your undergraduate or graduate degree, document it. What was the question being asked, and how did you answer it? What technologies did you use? What were the results? What were the challenges you had to overcome and how did you overcome them? Even small projects can yield a wealth of information and highlight your thought process and troubleshooting skills as well as your ability to deliver results.
Another item hiring managers look for is passion projects. These are activities you do outside of work or school either because you are curious, you like to tinker, or because you are really excited about a particular domain. These demonstrate that you are willing to go the extra mile for something you care about, and these projects can highlight your skills at the same time. You should focus on cultivating a github presence with public resources that hiring managers can evaluate to get a feel for your skills.
Cultivating an online presence beyond GitHub can be an asset, too. Whether it’s a dedicated blog, or posts to LinkedIn or other platforms, any record of your activities that are Computer Vision-related will help hiring managers get comfortable hiring you even if you don’t have a wealth of experience.
If you can’t think of something you’re passionate about that presents a computer vision opportunity, head over to kaggle.com for some ideas. They host competitions that you can join with curated datasets and clear parameters for participation. If you tackle one or more of these projects, you can describe them the same way as academic work described above to give potential employers a feel for the kind of work you do.
Finally, look for alternative means of gathering experience outside of the typical 9 to 5 gig. This could involve consulting on a platform like Upwork, or looking for volunteer opportunities or an internship through your network.
Once you’ve accumulated enough projects to demonstrate what you’re capable of, it’s time to package them all up in a compelling format. In my earlier post I described how important it is to craft a brief and concise resume. It can be very helpful to create a companion presentation to your resume to help bring specific projects to life. This presentation of your Computer Vision portfolio should be visual and appealing. It should describe the context, activities and outcomes of your projects.
Photo by Leio McLaren on Unsplash
About the Author
Erskine Williams began his career as an engineer at Intel, before moving on to write molecular modeling software for Fujitsu BioSciences. Erskine then grew professional services revenue at Jive Software 100% year over year for four years before Jive’s IPO in 2011. Most recently, Erskine held key engineering leadership positions at eBay as Director of Mobile Architecture and Head of Americas Regional Development. Erskine’s 20+ years of engineering and product management experience ensure Synaptiq’s clients achieve their objectives with high impact AI-enabled solutions. Erskine has a B.A. in Cognitive Science and Biochemistry from the University of Virginia. He enjoys fly fishing, mountain biking, and skiing.
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