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Computer Vision Jobs in Chicago

At Harnham, we understand the importance of a strong Computer Vision team who can develop bespoke algorithms and continuously innovate. We make it our mission to utilize our vast Data & Analytics knowledge to deliver a professional and quality experience.

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Computer Vision Offers Safety and Security in Surprising Industries

At a Yale University speech several years ago, Peter Thiel, the founder of PayPal joked, “We wanted flying cars, instead we got 140-characters”. Well, flying cars are still in the future, and so are self-driving cars. Yet, some autonomous vehicles have found homes in the most unlikely of industries.  The rules and regulations which keep our roads safe are also hindering our ability to realize self-driving cars. Yet, safety measures abound ready to ‘plug-and-play’ the safe handling of you in the driver’s seat and those with whom you share the road. Hands off the steering wheel, of course. Three Ways Computer Vision is Preparing for Driverless Cars 3-D Mapping for RealTime Learning – much like your backing camera on your latest automobile, car cameras can also record live footage to map their environment. From this Data, autonomous vehicles can spot obstacles or determine alternate paths.Sensing Obstacles and Objects – using sensors to determine what the obstacle or object in the road is – whether it’s pedestrians, other vehicles, or even something as simple as a loose bag or cardboard flap. If it’s something you’d have to drive around to avoid hitting, shouldn’t your car know this, too?Gathering Detailed Data – can help your self-driving vehicle identify traffic lights, road conditions, and congestion. Each of these elements are steps to a more reliable experience, once driverless cars come on the scene. In the meantime, there’s an old industry bringing machine and human together like never before. Building for the future is employing robotics, AI, and Computer Vision technologies for seamless integration. Building Technology: Computer Vision Meets Construction Sites It’s backbreaking work to move dirt from one place to another, but if you’re going to build, it’s the first thing to be done. It’s also the most repetitious and mundane. Enter autonomous heavy equipment. These machines prepare the sites for the human crews who will come in later to do the building itself. Before panic sets in that robots are replacing people, understand that people can still move faster than these large machines. The idea behind automating processes is to ensure projects remain on schedule using consistent, reliable resources; man and machine working together. Yet, there is one place where man shines and machine does not. Controlled chaos and changing conditions. The Computer Vision elements employed here can help systems to recognize things such basics as utility lines and variances such as historical artifacts. Finding something like an archeological site or historical artifact can stall or stop a project. But whether the site’s on track to finish on schedule or a glitch throws a curveball into the schedule, the site still needs to be protected. Who better than a drone? Safety First – Construction Site to Driver’s Seat Autonomous vehicles whether on the road or in the sky offer a unique view of their environment. Just as driverless cars are employing 3D mapping and object identification, drones are being used to help navigate and manage construction-size projects. Below are a few ways they’re making waves: Predictive Modelling using Computer Vision - predict how much on-site material may be needed.Put together prefabricated partsTrack progress and watch for things like structural issues, number of trucks entering a site, even if teams are following best practices. Though driverless cars are still future forward ideas, driverless trucks, and other autonomous heavy equipment are in the driver’s seat. Making the idea of working with machines exciting to the professionals in the industry is one way to make the idea more palatable. The move to intelligent, more reliable systems to keep projects and people on track, on budget, safe, and to ultimately solve a problem offers bold solutions for the future. If you’re interested in Big Data, Analytics, Life Sciences, and more opportunities in the Data professional’s industry, Harnham may have a role for you. Check out our current vacancies or get in touch with one of our expert consultants to learn more: For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

Mairead Holt

Mairead Holt

10. December 2020

Why You Should Always Be Learning In Data Science: Tips From Kevin Tran

Last month we sat down with Kevin Tran, a Senior Data Scientist at Stanford University, to chat about Data Science trends, improvements in the industry, and his top tips for success in the market.  As one of LinkedIn’s Top Voices of 2019 within Data & Analytics. his thoughts on the industry regularly garner hundreds of responses, with debates and discussions bubbling up in the comments from colleagues eager to offer their input.  This online reputation has allowed him to make a name for himself, building out his own little corner of the internet with his expertise. But for Tran, it’s never been about popularity. “It’s not about the numbers,” he says without hesitation. “I don’t care about posting things just to see the number of likes go up.” His goal is always connection, to speak with others and learn from them while teaching from his own background. He’s got plenty of stories from his own experiences. For him, sharing is a powerful way to lead others down a path he himself is still discovering.  When asked about the most important lesson he’s learned in the industry, he says it all boils down to staying open to new ideas.  “You have to continue to learn, and you have to learn how to learn. If you stop learning, you’ll become obsolete pretty soon, particularly in Data Science. These technologies are evolving every day. Syntax changes, model frameworks change, and you have to constantly keep yourself updated.”  He believes that one of the best ways to do that is through open discussion. His process is to share in order to help others. When he has a realisation, he wants to set it in front of others to pass along what he’s learned; he wants to see how others react to the same problem, if they agree or see a different angle. It’s vital to consider what you needed to know at that stage. Additionally, this exchange of ideas allows Tran to learn from how others tackle the same problems, as well as get a glimpse into other challenges he may have not yet encountered.  “When I mentor people, I’m still learning, myself,” Tran confesses. “There’s so much out there to learn, you can’t know it all. Data Science is so broad." At the end of the day, it all comes down to helping each other and bringing humanity back to the forefront. In fact, this was his biggest advice for both how to improve the industry and how to succeed in it. It’s a point he comes back to with some regularity in his writing. “It doesn’t matter how smart you are, stay humble and respect everyone,” one post reads. “Everyone can teach you something you don’t know.” Treating people well, understanding their needs, and consciously working to see them as people instead of numbers or titles—this, Tran argues, is how you succeed in the business. To learn and grow, you must work with people, especially people with different skills and mindsets. Navigating your career is not all technical, even in the world of Data. “The thing that cannot be automated is having a heart,” he tells me sagely. Beyond this, Tran stresses the need for a solid foundation. The one thing you can’t afford to do is take shortcuts. You have to learn the practicalities and how to apply them, but to be strong in theory as well.  Understanding what is happening underneath the code will keep you moving forward. He compares knowing the tools to learning math with a calculator. “If you take the calculator away, you still need to be able to do the work. You need the underlying skills too, so that when you’re in a situation without the calculator, you can still provide solutions.” By constantly striving to collaborate and improve, Tran believes the Data industry has the best chance of innovating successfully.  If you’re looking for a new challenge in an innovative and collaborative environment, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

William Reichert

William Reichert

19. March 2020

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