food scientists taking data science courses and extracting data in computer more effectively in food industry

Introduction

One of the questions I’m commonly asked by Food Science professionals is whether or not I think they should take continuing education courses to help their career prospects.
The answer is always yes.
That’s the easy part.
The important question is not “should I” take continuing education courses” – the more pertinent question is “WHAT continuing education courses should I take?”
And for me, that answer is easy as well.
To me, data science is the secret ingredient that, if you gain exposure to it, can significantly impact a food scientist’s career prospects.

Data Science and the Food Industry 

If you’re a Food Scientist who spends the majority of their work day between product concept and product commercialization, then you’re familiar with all shapes, sizes, volumes and sources of data.
Throw in all the different departments, people and processes involved in getting a product to market and well, it’s no wonder managing all that data on the way to getting a product to the store shelf is a messy, convoluted inefficient process.
But in recent years, a confluence of events, intermediaries and consumer sentiment have accelerated the appetite for a more transparent, simple to understand set of data from which the food industry can work with.  That’s where data science comes into play.
The explosion of plant-based food research and development and the follow-on competition to identify alternative proteins has led to an emphasis on data science.

Data Science applications

Take Dan Zigmond, the original Vice President of Data at JUST, and what his impact was waaay back in 2014.
Dan built a data science team at JUST to build the framework for a new, never seen before modern food brand.
Aside from building new compensation models for roles never before found in “traditional” food companies, negotiating leases and creating pricing and sales models for the company, Dan’s team set forth creating an online database that captures every characteristic and idiosyncrasy of each plant protein found on earth.
That’s a lot of data.
And that journey to comprehend and make actionable the voluminous gigs of data continues not just with JUST in Silicon Valley, but all of the alternative protein and plant-based food and beverage brands popping up globally.
As these new innovative products get closer to commercialization, the data that’s extracted from plant trials and manufacturing runs is critical – as the manufacturing process gains scale, the amount of protein, the characteristics of that protein and the interactions with other ingredients is an ever-changing data set.
That’s a tremendous amount of data that needs to be harnessed.
The same is going on at Tyson Foods, at Nestle and General Mills, and this is where the Food Scientist is, in some cases, getting boxed out by Process Engineers, Manufacturing, Innovation or Marketing when it comes to corralling all that disparate data.

Data Science and Food Scientists

It’s not the Food Scientist’s fault.
The majority of their Food Science curriculum was based on the traditional food system.
The educational system, like other institutions, did not and could not keep up with the advancements of the plant-based food research, or the confluence of technology and systems that would be able to generate the overwhelming volumes of data.
On top of that, consumer sentiment and behavioral science, typically an area of expertise more aligned with Marketing has changed so dramatically that many food scientists feel as if their role has been pulled out from underneath them.  They feel like they’re losing a turf war for access and control of the data.
But that doesn’t mean data science and food scientists don’t belong together – there’s a huge opportunity to evolve.

Options for a Food Scientist to learn and leverage Data Science

The important consideration is to understand WHY you’re learning about data science.
It’s NOT to become a data scientist per se.
It’s to become an effective consumer and communicator of data in specific environments that hold high value within a food or beverage company.
That could mean taking the reins on moving sensory and consumer insights data in-house from a 3rd party.
It could mean serving as Program Manager for a brand moving its pilot plant in-house and importing the manufacturing data from the current contract manufacturer.
It could mean consolidating disparate data warehouses from a recent acquisition and setting up a new ingredient and innovation platform for your company.

Conclusion

See, the thing that I always try to convey to Food Scientists is these projects HAVE to get done….by someone.
It could be someone in Marketing who takes the stretch project for themselves.
Or it could be a new hire who the company has to overpay for.
Or it could be one of your peers.
Or it could be you.
Once you’ve got a project like this under your belt, you’ve effectively moved from being perceived as an individual contributor / specialist to a high-impact problem solver.
And you’ve opened yourself up to significantly more career opportunities, laterally and upward, in the food and beverage industry as well as adjacent(broader consumer packaged goods).
I’m a big fan of professional development and continuing education platforms that are available today like Udacity

 

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