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    November 10, 2022

    The Evolution of Today’s Lab Automation Scientist

    Emilie Fritsch, Principal Scientist at Syngenta, joins Fane to talk about her journey from scientist to lab automation specialist. As her career evolved, so did her understanding of the automation needs of science. She went from molecular biology to the perfect blend of science and automation, learning the best of both worlds to shape better science.

    The podcast is filled with nuggets of wisdom to take away, here’s what you can expect.

     (3:00 mins) Being thrown into the deep end of lab automation

    It feels like common stance that most scientists just “fall” into doing lab automation because they’re naturally curious and geared toward solving problems with technology. That nature doesn’t always prepare you for what you’re about to learn.

    Because it’s daunting to have to learn a new platform, be it software or hardware. Thankfully with the help of application scientists, learning a new platform becomes a matter of shifting your mindset and knowing when to use manual or automated techniques.

    Her journey into lab automation piqued other scientists' interest in lab automation, and in trying new technologies. It was rewarding for Emilie to see scientists interested in automation and compensated for all the frustrations she had experienced with lab automation.

     (7:30 mins) Three users of new technology: early adopters, watchers and skeptics

    Being a field application scientist means different things to different companies but the guiding star as a field application scientist is that you’re there to help your users understand the labware they’re using and support them when things don’t go well. Especially with the uncertainty that comes with biology. This does wonders for promoting confidence in the labware.

    This mindset then translates into the types of users that you’ll get when adopting new technologies. Early adopters are the first in and want to test things really quickly.  Watchers are those that aren’t against the technology but want to see how it goes first and then jump on board. Then you’ll have the few who resist slightly - the skeptics. They’re healthy to have around because they push you to show with data how the tech works and that this is the best approach.  

    These personas should be used in mind when you’re implementing new technology and who you want to target for what. Use early adopters to test new tech rather than bringing everyone on board in one go. Then implement in step so you can develop confidence in tech and the team. Don’t go from 0 to 100%, go with a phased approach.

    (11:40 mins) Communicate reassurance. Look after those that are resisting new technologies and they will become your champions

    Speaking to the three users of new technology adoption requires a different approach to communication. You don’t want to scare off the more resistant which means you may need to slow down. This can be frustrating for the early adopters because they’re enthusiastic and want everything in one go.

    Early adopters also need structure. Structure to feed the amazing enthusiasm. And for the skeptics, hold their hands and show them that it can be done and spend time with them in the lab. Reassurance that they will always be supported by your experts in the new technology.

    As the experts, you can benefit from the growth. As they progress and grow - they become your champions because they’ve seen the new technology work firsthand with success.

    (14:11 mins) The “Eureka” moment of a skeptic adopting new technology feels really good

    You know that skeptic that works in your team. They didn’t really know the value themselves. But in Emilie’s case, she was surprised to hear that skeptic message her one day and say “Hey, I’ve run this by myself.” It's a great feeling, and the teachable moment here is that sometimes skeptics have to learn by themselves as well.

    (15:43 mins) The key learning from automation: not one size fits all

    Whilst there’s a diversity of different tools from hardware to software, there’s not one solution that fits all. Typically they buy one really big robot thinking that it will handle most of their use cases but it becomes too complex when the actions can be more simple.

    The sweet spot Emilie talks about is that there are different components to your solution and that you need to understand what the best mix of hardware and software is to help you do the science you want to do.

    (21:43 mins) Understand your problem so you know when to use automation

    You don’t need to use automation for everything. In some cases, you can’t apply automation because the biology can’t be reproduced easily, and you have a limited budget because the process is complex. So really understanding your problem and understanding your limits, especially with hardware and software is key to knowing when to use automation and how to maximize each opportunity.

    (23:05 mins) Biologists need to spend less time pipetting and more time on the things they’re good at - the science

    Scientists - especially biologists, are proud of being good pipettors. This is important especially when you can do qPCRs in 384 well plates. But if you can use a robot, then use it to do a qPCR. But the reality is that biologists are not good at their job because they’re good pipettors, but because they’re creative, know how to analyze data, and think about how to design experiments which is much more valuable.

    What automation does, is take away the arduous time spent pipetting in the lab and spending time on the more valuable skills that they have. Now, scientists are realizing the power of automation and how valuable it is to them.

    (27:50 mins) Scientists need to be good communicators to thrive in a diverse environment filled with opportunities and challenges

    Emilie has always loved languages and has learned a few already in high school. So she’s naturally inclined to learn languages. But scientists need to become good communicators and the ship has sailed on the era of “getting the results all by yourself”.

    The challenges are much bigger and more complex and you need skill sets that can handle all the different aspects of that challenge. This is why it's advantageous to work in a diverse environment where people have different experiences and will have different points of view to help develop different solutions.

    Diversity helps you become a good communicator. Especially different backgrounds. Emilie’s experience with biologist and software engineers have different ways of looking at a problem (scientists often being more cynical).

    (31:43 mins)  Women can become lab automation specialists, your study doesn’t define your scientific future.

    The next generation of life science leaders needs to know that your study doesn’t define your direction. When Emilie was finishing her Ph.D., she didn’t remember seeing opportunities for female automation scientists. She counts herself really lucky because she was able to learn as she progressed in her career. But if she had known there were these options a lot earlier in her career, she might have gotten to where she is now, quickly. It was a laughable concept earlier in her career.

    It’s not just a man's job just because you’re working with robots, women can work in lab automation. But also allow you to grow and evolve into the role you want to be. Be creative, especially in a male-dominated field. It’s not easy, but it's getting better. We’re seeing more representativity with female leaders in life sciences.

    Listen on to follow the conversation. If you have questions about this podcast - is only an email away!

    Tag(s): Lab Automation

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