Gastrograph AI is an artificial intelligence platform for the food and beverage industry. Our technology enables food and beverage producers to update, formulate, and optimize new and existing products to meet changing consumer preferences in demographics around the world.
Domain SME: Our Domain Subject Matter Experts are production experts, usually hired from the industries in which AFS has clients. (i.e. a Brew Master for beer or Floor Manager for chocolate production)
On-Site Technical Project Manager: Our on-site team integrates with our clients’ production, R&D, and/or quality staff to successfully implement our technology into their day-to-day operations. Individuals on the on-site team may be assigned to a rotation, an industry, a client, or a specific production center depending on their expertise and the scope of our work.
R&D Team: Our R&D team works across a variety of functions to test, train, and improve our platform, technology, and domain expertise. The R&D team is responsible for the training and updating of our vertical process maps, the operations of our Food and Beverage Production R&D Lab, and creating the baseline datasets and implementation plans for the company to enter new verticals within the food and beverage industry.
Interns, by nature of the work involved, are only eligible for the R&D Team.
AFS uses a standardized technical project to gauge every candidate’s fit for a position on our team. As the same technical interview is used for interns and full-time applicants across multiple roles, we adjust our expectations and rubric accordingly. Our primary goal is to gauge your ability to understand the implementation, application, and value of our technology from conversations or publicly available information – and to think critically on how to build, apply, and improve our platform for our clients.
Analytical Flavor Systems frequently enters new verticals of food and beverage production, requiring the creation of a new expert-constructed process map detailing the production pathway of that verticals products.
You will be assigned a product vertical AFS is already active in. Your goal is to:
1) Map the process for producing your assigned product. Encode the knowledge of your production process into a computer readable format. For example, in the production of tea, all tea is first picked, withered, and then further processed. Include metrics, opportunities for data collection, and points of control accompanying each production step. For example, in the case of tea withering: time, temperature, ambient humidity, etc.
2) Select a single production step of interest:
a. Indicate the options for parameters at that production step and note how those options will change the resultant flavor profile
b. Extend the production map at the selected production step to include possible issues that can occur (in the example of tea withering, rainy weather after harvesting can make it difficult for the tea to properly lose ~80% of it’s moisture content) and the possible flaws that can occur such as specific chemical compounds or other sensory or physical defects (to continue this example, tea processed on a rainy day, often has to be heated more during withering to aide in the removal of excess moisture, which can sometimes give the tea a smoky taste)
c. Show how those issues and flaws can be predicted at any step earlier in the process (in the example of tea withering, predictive metrics such as weather reports and trends could have been used to optimize the harvest window).
d. Assuming that the algorithm we create to predict the issue or flaw has failed, or that a specific flaw or issue could not have been predicted, and that flaw has formed – show how that issue or flaw can be identified in real time (which combination of variables would be indicative of its presence?)
e. Further extend the production map at your production step of interest to include a mitigation strategy to negate the occurrence of the issue or flaw when predicted beforehand (preemptive mitigation).
f. Finally, extend the production map one last time to include a mitigation strategy to negate the effect of the issue or flaw when identified as present (reactionary mitigation).
We use graphical databases for our encoding in production environments.You may use excel or any other format that fits your idea for an optimally complete interview project. It’s definitely not required, but if you wanted to do something super impressive, do this in a graphical database (just not Neo4J).
1) The process map and all extensions
a. Instructions to run or view the process map if hosted in a database
2) Any list of assumptions you make while developing this process map
a. This includes things such as limitations on scope, or the purposeful exclusion of a sub-class within the product vertical that follows a different production pathway
3) A brief write-up in PDF format on how you would test, update, and apply your production map to optimize your selected process in a client’s production environment.
4) Should you pass the technical interview on steps 1 – 3, we will fly/conjure/taxi you to our office to meet the team, and present your findings to both data scientists and chemistry team members.
There is no deadline for full-time applications. We accept submissions on a rolling basis, and are almost always hiring for our growing team. Summer interns should apply by the last day in April.
There are no rules - feel free (or only slightly constrained) to expand or modify this interview to fit your special skill set. If it showcases your skills and we find it impressive, it would greatly benefit your application.
An interactive query app to see the process options and pathway changes, a math-heavy version of the process control system, or predicted flavor outcomes – use one of these or come up with your own.
At AFS, entering a new vertical is a team effort. The AFS team is happy to collaborate with, answer questions, and point you in the right direction. Feel free to hit us up.
Please email this interview to:
Best of luck! - Jason (CEO) & Ryan (R&D Manager)Read more
Analytical Flavor Systems | Manhattan - NYC | Full-Time | Onsite |
Analytical Flavor Systems uses machine learning and artificial intelligence to build tools for the food & beverage industry. Our Quality, Process, and Market Intelligence services create real-time predictive decisions metrics at each stage of a products life-cycle. We leverage our predictive models across products & industries for flavor profile optimization, production process optimization, demographic targeting & cognitive marketing - helping companies create and sell the best product to their highest value consumers with every batch.
-Quality Intelligence: Real-time predictive quality control, assurance, and improvement from human sensory data.
-Process Intelligence: Real-time predictive process control and optimization from human sensory data + manufacturing & LIMS data.
-Market Intelligence: Linking flavor-profile, demographics, and sales data to find the highest value consumer demographics for a product's flavor-profile.
Position: Full-Stack Engineer
Role: Web-application or Streaming Infrastructure focused full-stack engineer capable of integrating the data pipeline and outputs of machine learning models into an easy to use management platform.
Bonus: -Experience designing and maintaining streaming infrastructures -Experience with TensorFlow
Team: We're a diverse 6-person company (across data, engineering, chemistry, design and biz) that is passionate about high-quality food and beverage products.
Next Steps: Please submit something awesome to JasonCEO@Gastrograph.com to apply.Read more
Sour beers are where beer started. Spontaneous fermentation was the original way to introduce alcohol-producing microbes into the wort. Spontaneous fermentation is achieved by simply leaving the fermenting wort open to the air. This is how the first beers were made and how beer was made until it was discovered that yeast were responsible for the transformation of sugars to alcohol. Once microbes were discovered, it was only a matter of time before a strain of yeast was isolated and used to make more consistent, clean-flavored beer. This marked the decline of sour beer - drinkers wanted a more predictable flavor. But now sour beer is on the rise and some consumers can’t get enough of these tangy tart flavors.Read more