International firm Cognizant acquires ATG, plans to grow Missoula workforce

Montana CEOs Share Four Strategies for Hiring More Women in Tech and Manufacturing

By Christina Henderson

As a female programmer in Montana, Lindsey Hanna is used to being in the minority. While earning her bachelor’s degree in Computer Science at Montana State University (class of 2012) and during her six years as a Software Engineer at Workiva, only around 10 percent of her colleagues have been women.

“When I started working [at Workiva] there were 35 people in the Bozeman office and I think 30 of them were male,” Hanna said. “And that’s just a more intimidating environment to work in.”

Compounding the gender imbalance was the fact that many of Hanna’s colleagues didn’t realize it was a issue.

“There might be only one girl on a team of 23, but people will say, ‘It’s a good thing we don’t have a diversity problem,” Hanna said.

Two years ago, things started to change.

Workiva sent four women to the Grace Hopper Conference, an event for women in computing that attracts 15,000 people – 14,000 of them women – sparking a big push to improve gender balance in software engineering.

“There was buy-in immediately at the executive level,” Hanna said.

According to Hanna’s boss, Jeff Trom, CTO and co-founder of Workiva and board chair of the Montana High Tech Business Alliance, diverse engineering teams aren’t just more comfortable for women, they make better products.

“That’s been proven over and over again,” Trom said. “It hurts our bottom line not to have diverse teams. We have made a commitment to make it happen. But it’s tough.”

Initiatives at Workiva included forming an affinity group for women with an active chat room, implementing hiring practices to avoid scaring away diverse candidates, and sponsoring the Women in Engineering dinner at MSU. Workiva also released the gender breakdown of its workforce internally, which prompted people to recognize this was a situation at a company level.

“The main thing we have done is start talking about it,” Hanna said. “That’s an important first step.”

Workiva is one of many member companies in the Montana High Tech Business Alliance addressing the lack of women in technical roles in our workforce. Top managers from across the state met at Foundant Technologies in Bozeman in February to share the challenges they have faced, and the steps they have taken to fix the problems.

Click the link below to read four takeaways from the discussion:

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Blackmore's new doppler lidar solves self-driving cars' need for speed

As every New Yorker knows, San Franciscans walk painfully slowly. It's a common observation, and now it's a measurable one, thanks to the lidar laser scanners stuck on the roof of the van that's currently steering me though rush hour traffic in downtown SF.

I’d been on plenty of these lidar safaris before, but this was the first that showed not just the pedestrians, cyclists, and other vehicles around us, but the velocity of each. Displayed not in numbers, though, but in colors. The things moving away from us are in red, those moving toward us are in blue. The faster they’re going, the darker the hue. Stephen Crouch points out the crimson limbs of a yellow-torsoed pedestrian. “You see on the legs, they’re darker,” he says—the feet being the fastest moving bit of a person walking.

Crouch is a cofounder and the CTO of Blackmore, the Montana-based startup that made this speed-spotting lidar system. It’s an advance with the potential to help self-driving cars see, understand, and glide through a crowded world more easily than ever before.

Velocity, you see, is the easiest way to determine what is moving where, pretty important information for a robot trying to determine what merits its attention. Radar can measure velocity—that underpins adaptive cruise control and systems like Tesla’s Autopilot—but its resolution is terrible. Essentially, it can tell you, Something 50 meters that way is coming at you at 62.38 mph. Could be a truck, could be group of cows falling out of a truck.

Lidar’s resolution is far better, but conventional systems can only provide indirect velocity data: You have to watch how far things move over time, comparing an image taken at one moment to the one taken milliseconds later. The problem there is that today’s systems, for all their laser points, don’t hit everything. Think of it like a game of Battleship—you need your laser to actually hit an object to know it’s there. That’s hard enough, especially at long distances. But you can only measure where something’s going, and how fast, if you manage to hit it again and again.

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