JasonCEC

Analytical Flavor Systems | Manhattan - NYC | Full-Time | Onsite | http://www.Gastrograph.com/

Position: Full-Stack Engineer, Application Engineer, DevOps, Data Scientist, Sales (inside or field)

Application & Data Stack: Golang, Javascript, Docker, Streaming Infrastructure, R, TensorFlow, MySQL, AWS

Team: we're a diverse 6 person company (across Data, Engineering, Chemistry, and Biz)

Analytical Flavor Systems has developed an AI platform for the development and optimization of food and beverage products. Our Innovation Management, Deep Market Insights, and Quality Intelligence services create real-time predictive decisions metrics at each stage of a products life-cycle - from conception to consumption - helping companies create and sell the best product to their highest value consumers.

Our Services

_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.

The Position(s)

_Engineering_: 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.

_Data Science_: Data science is central to our predictive Quality, Process, and Market Intelligence services. We didn’t build a data science team to optimize our product's marketing spend, sales funnel, or client retention – we built a data science team to build our product. We need data scientists who can understand our clients and can take a nebulous business goal, create a set of quantitative decision metrics, and build predictive models to optimize those metrics. The extensive role of data scientists at Analytical Flavor Systems allows us to invest in their education across sensory perception (standard sensory science so they know what we’re improving and replacing), tasting experiences (so they appreciate the products we work on and understand how the data is collected), production knowledge (test batches in our R&D brewery and roastery so they understand the data they work with and how our predictions impact a client’s process), and data science tear-downs (a meeting where the team collaboratively attempts to find and fix problems, try new techniques, and debate the philosophical implications of a model's construction).

_Sales_: We prefer the thoughtful relationship builder to the cowboy negotiator. Most of our contracts are multi-year high-price affairs, so relationships are really important.

Next Steps Please submit something awesome to JasonCEO@Gastrograph.com to apply.

My company[1] Analytical Flavor Systems is a vertically integrated domain-expert based AI for new product development, flavor profile optimization, and predictive manufacturing in the food and beverage industry.

Like the article and some of the comments here suggest, it took years of domain expertise (most of the team comes out of the Tea Institute at Penn State, a research Institute for tea and tea tasting), followed by years of R&D to collect the proprietary data-sets and develop the models. And then it took a year or so to build a product around the AI's predictive capabilities - this isn't the shortest or easiest path, but we're still going strong!

I think companies like this are hard to build, hard to fund, and hard to compete with.

Where I disagree with other comments is on the competitive side; we've developed a few of our own algorithms[2] (not generic or even "played with some options" neural nets / deep learning) trained on specialized and proprietary data set from years of work and collection - now that we've dug our moat, I don't think anyone will be competitive with out specialized AI for modeling human sensory perception and predicting preferences[3] of food and beverage products anytime soon!

[1] www.Gastrograph.com

[2] https://gastrograph.com/resources/whitepapers/local-fisher-d...

[3][PDF] https://gastrograph.com/resources/whitepapers/2017-market-pr...

Analytical Flavor Systems | Manhattan - NYC | Full-Time | Onsite | http://www.Gastrograph.com/

Position: Full-Stack Engineer, Application Engineer, DevOps, Data Scientist, Sales (inside or field)

Application & Data Stack: Golang, Javascript, Docker, Streaming Infrastructure, R, TensorFlow, MySQL, AWS

Team: we're a diverse 9 person company (across Data, Engineering, Chemistry, and Biz)

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.

Our Services

_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.

The Position(s)

_Engineering_: 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.

_Data Science_: Data science is central to our predictive Quality, Process, and Market Intelligence services. We didn’t build a data science team to optimize our product's marketing spend, sales funnel, or client retention – we built a data science team to build our product. We need data scientists who can understand our clients and can take a nebulous business goal, create a set of quantitative decision metrics, and build predictive models to optimize those metrics.

The extensive role of data scientists at Analytical Flavor Systems allows us to invest in their education across sensory perception (standard sensory science so they know what we’re improving and replacing), tasting experiences (so they appreciate the products we work on and understand how the data is collected), production knowledge (test batches in our R&D brewery and roastery so they understand the data they work with and how our predictions impact a client’s process), and data science tear-downs (a meeting where the team collaboratively attempts to find and fix problems, try new techniques, and debate the philosophical implications of a model's construction).

_Sales_: We prefer the thoughtful relationship builder to the cowboy negotiator. Most of our contracts are multi-year high-price affairs, so relationships are really important. Plus, you get to spend your time at breweries, distilleries and roasteries (I've personally never been to a sales meeting where beer or coffee wasn't served freshly brewed).

Next Steps

Please submit something awesome to JasonCEO@Gastrograph.com to apply.

Analytical Flavor Systems | Manhattan - NYC | Full-Time | Onsite | http://www.Gastrograph.com/

Position: Full-Stack Engineer, Application Engineer, DevOps, Data Scientist, Sales (inside or field)

Application & Data Stack: Golang, Javascript, Docker, Streaming Infrastructure, R, TensorFlow, MySQL, AWS

Team: we're a diverse 9 person company (across Data, Engineering, Chemistry, and Biz)

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.

Our Services

_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.

The Position(s)

_Engineering_: 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.

_Data Science_: Data science is central to our predictive Quality, Process, and Market Intelligence services. We didn’t build a data science team to optimize our product's marketing spend, sales funnel, or client retention – we built a data science team to build our product. We need data scientists who can understand our clients and can take a nebulous business goal, create a set of quantitative decision metrics, and build predictive models to optimize those metrics.

The extensive role of data scientists at Analytical Flavor Systems allows us to invest in their education across sensory perception (standard sensory science so they know what we’re improving and replacing), tasting experiences (so they appreciate the products we work on and understand how the data is collected), production knowledge (test batches in our R&D brewery and roastery so they understand the data they work with and how our predictions impact a client’s process), and data science tear-downs (a meeting where the team collaboratively attempts to find and fix problems, try new techniques, and debate the philosophical implications of a model's construction).

_Sales_: We prefer the thoughtful relationship builder to the cowboy negotiator. Most of our contracts are multi-year high-price affairs, so relationships are really important. Plus, you get to spend your time at breweries, distilleries and roasteries (I've personally never been to a sales meeting where beer or coffee wasn't served freshly brewed).

Next Steps

Please submit something awesome to JasonCEO@Gastrograph.com to apply.

Analytical Flavor Systems | Manhattan - NYC | Full-Time | Onsite | http://www.Gastrograph.com/

Position: Full-Stack Engineer, Application Engineer, DevOps, Data Scientist, Sales (inside or field)

Application & Data Stack: Golang, Javascript, Docker, Streaming Infrastructure, R, TensorFlow, MySQL, AWS

Team: we're a diverse 6 person company (across Data, Engineering, Chemistry, and Biz)

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.

Our Services

_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.

The Position(s)

_Engineering_: 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.

_Data Science_: Data science is central to our predictive Quality, Process, and Market Intelligence services. We didn’t build a data science team to optimize our product's marketing spend, sales funnel, or client retention – we built a data science team to build our product. We need data scientists who can understand our clients and can take a nebulous business goal, create a set of quantitative decision metrics, and build predictive models to optimize those metrics.

The extensive role of data scientists at Analytical Flavor Systems allows us to invest in their education across sensory perception (standard sensory science so they know what we’re improving and replacing), tasting experiences (so they appreciate the products we work on and understand how the data is collected), production knowledge (test batches in our R&D brewery and roastery so they understand the data they work with and how our predictions impact a client’s process), and data science tear-downs (a meeting where the team collaboratively attempts to find and fix problems, try new techniques, and debate the philosophical implications of a model's construction).

_Sales_: We prefer the thoughtful relationship builder to the cowboy negotiator. Most of our contracts are multi-year high-price affairs, so relationships are really important. Plus, you get to spend your time at breweries, distilleries and roasteries (I've personally never been to a sales meeting where beer or coffee wasn't served freshly brewed).

Next Steps

Please submit something awesome to JasonCEO@Gastrograph.com to apply.

Analytical Flavor Systems | Manhattan - NYC | Full-Time | Onsite | http://www.Gastrograph.com/

Position: Full-Stack Engineer, Application Engineer, DevOps, Data Scientist, Sales (inside or field)

Application & Data Stack: Golang, Javascript, Docker, Streaming Infrastructure, R, TensorFlow, MySQL, AWS

Team: we're a diverse 6 person company (across Data, Engineering, Chemistry, and Biz)

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.

Our Services

_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.

The Position(s)

_Engineering_: 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.

_Data Science_: Data science is central to our predictive Quality, Process, and Market Intelligence services. We didn’t build a data science team to optimize our product's marketing spend, sales funnel, or client retention – we built a data science team to build our product. We need data scientists who can understand our clients and can take a nebulous business goal, create a set of quantitative decision metrics, and build predictive models to optimize those metrics.

The extensive role of data scientists at Analytical Flavor Systems allows us to invest in their education across sensory perception (standard sensory science so they know what we’re improving and replacing), tasting experiences (so they appreciate the products we work on and understand how the data is collected), production knowledge (test batches in our R&D brewery and roastery so they understand the data they work with and how our predictions impact a client’s process), and data science tear-downs (a meeting where the team collaboratively attempts to find and fix problems, try new techniques, and debate the philosophical implications of a model's construction).

_Sales_: We prefer the thoughtful relationship builder to the cowboy negotiator. Most of our contracts are multi-year high-price affairs, so relationships are really important. Plus, you get to spend your time at breweries, distilleries and roasteries (I've personally never been to a sales meeting where beer or coffee wasn't served freshly brewed).

Next Steps

Please submit something awesome to JasonCEO@Gastrograph.com to apply.

Analytical Flavor Systems | Manhattan - NYC | Full-Time | Onsite | http://www.Gastrograph.com/

Position: Full-Stack Engineer, Application Engineer, DevOps, Data Scientist, Sales (inside or field)

Application & Data Stack: Golang, Javascript, Docker, Streaming Infrastructure, R, TensorFlow, MySQL, AWS

Team: we're a diverse 6 person company (across Data, Engineering, Chemistry, and Biz)

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.

Our Services

_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.

The Position(s)

_Engineering_: 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.

_Data Science_: Data science is central to our predictive Quality, Process, and Market Intelligence services. We didn’t build a data science team to optimize our product's marketing spend, sales funnel, or client retention – we built a data science team to build our product. We need data scientists who can understand our clients and can take a nebulous business goal, create a set of quantitative decision metrics, and build predictive models to optimize those metrics.

The extensive role of data scientists at Analytical Flavor Systems allows us to invest in their education across sensory perception (standard sensory science so they know what we’re improving and replacing), tasting experiences (so they appreciate the products we work on and understand how the data is collected), production knowledge (test batches in our R&D brewery and roastery so they understand the data they work with and how our predictions impact a client’s process), and data science tear-downs (a meeting where the team collaboratively attempts to find and fix problems, try new techniques, and debate the philosophical implications of a model's construction).

_Sales_: We prefer the thoughtful relationship builder to the cowboy negotiator. Most of our contracts are multi-year high-price affairs, so relationships are really important. Plus, you get to spend your time at breweries, distilleries and roasteries (I've personally never been to a sales meeting where beer or coffee wasn't served freshly brewed).

Next Steps

Please submit something awesome to JasonCEO@Gastrograph.com to apply.

Analytical Flavor Systems | Manhattan - NYC | Full-Time | Onsite | http://www.Gastrograph.com/

Position: Full-Stack Engineer, Application Engineer, DevOps, Data Scientist, Sales (inside or field)

Application & Data Stack: Golang, Javascript, Docker, Streaming Infrastructure, R, TensorFlow, MySQL, AWS

Team: we're a diverse 6 person company (across Data, Engineering, Chemistry, and Biz)

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.

Our Services

_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.

The Position(s)

_Engineering_: 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.

_Data Science_: Data science is central to our predictive Quality, Process, and Market Intelligence services. We didn’t build a data science team to optimize our product's marketing spend, sales funnel, or client retention – we built a data science team to build our product. We need data scientists who can understand our clients and can take a nebulous business goal, create a set of quantitative decision metrics, and build predictive models to optimize those metrics.

The extensive role of data scientists at Analytical Flavor Systems allows us to invest in their education across sensory perception (standard sensory science so they know what we’re improving and replacing), tasting experiences (so they appreciate the products we work on and understand how the data is collected), production knowledge (test batches in our R&D brewery and roastery so they understand the data they work with and how our predictions impact a client’s process), and data science tear-downs (a meeting where the team collaboratively attempts to find and fix problems, try new techniques, and debate the philosophical implications of a model's construction).

_Sales_: We prefer the thoughtful relationship builder to the cowboy negotiator. Most of our contracts are multi-year high-price affairs, so relationships are really important. Plus, you get to spend your time at breweries, distilleries and roasteries (I've personally never been to a sales meeting where beer or coffee wasn't served freshly brewed).

Next Steps

Please submit something awesome to JasonCEO@Gastrograph.com to apply.

Analytical Flavor Systems[0].

We built a AI for beer flavor profile consistency and quality optimization. NVIDIA wrote an awesome article about us here[1].

More Info

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.

Our Services

__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.

[0]www.Gastrograph.com

[1] http://blogs.nvidia.com/blog/2015/09/02/beer/

Ouch man.... not all craft beer software is BS CRUD apps overbuilt on react.js by 22 year old hipsters deploying single function "micro-services" each in their own docker container...

Brewing beer is a form of manufacturing, and most manufacturing can be made better or more efficient though targeted software.

[0] commenting because I don't want "craft beer software" to become a joke. Some of us actually work in that industry.

I actually work in this industry[0], with a few of the major brands, and... it's 80% marketing.

Unlike beer, where smaller is often "better" (as in more flavorful, more innovative, more unique, more interesting - but definitely not more consistent);

Most of the new "distilleries" are actually non-distilling bottlers - simply taking (and sometimes ageing) distilled spirits from MGP[2] (the former Seagram Distillery).

Consider - how can a ~5 year old brand have bottled and distributed 15 year old or 25 year old spirits?

This is a real problem in the industry, as 1) it's false marketing, and 2) the equipment needed to make great spirits is not nearly as affordable, and is much more a function of scale, then it is in other industries. So the craft distillery with the 12 foot still and 5 plates really can't compete with the large distillery with a 35 foot still and 15 plates (that's the level of control they have over their process and thus their flavor profile).

And thus, with these "fake" craft spirits - it's the consumers (you and I) that get screwed - we pay extra for a fake premium brand available for much less - the identical spirit, on the same shelf, with the same flavor profile, just under a different name.

~~~~~~~~~~~~~~~

and in case anyone is interested: my company helps real distilleries, breweries, and coffee / chocolate makers, optimize their flavor profiles and consistency through predictive manufacturing[1]. Feel free to reach out![3]

~~~~~~~~~~~~~

[0] www.Gastrograph.com

[1] off by one error

[2] http://www.thedailybeast.com/articles/2014/07/28/your-craft-...

[3] jasonceo [at] gastrograph [dot] com

My company[1] builds machine learning and AI based quality control and production optimization for food manufacturing...

Would consumers be interested in a food verification service?

For the most part, we don't / wouldn't deal in food-safety, but we could do things like verify the age and provenience of wine, or the terroir of coffee.

Discuss here or shoot me an email[2]!

[1] www.Gastrograph.com [2] jasonCEO [at] ^

I wrote an analysis on cold brewed coffee's a while back: https://www.gastrograph.com/blogs/gastronexus/flavor-profile...

Cheers!

Analytical Flavor Systems | Middle of Nowhere Pennsylvania (State College, moving / more offices soon) | Full-Time | Onsite | http://www.Gastrograph.com/

Position: Full-Stack Engineer |or| Data Scientist |or| Sales (inside or field)

Application & Data Stack: Golang, Javascript, Docker, R, TensorFlow, MySQL, AWS

Team: we're a diverse 7 person (all technical) company (across Data, Engineering, and Chemistry)

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.

Our Services

_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.

The Position(s)

_Engineering_: Web-application focused full-stack engineer capable of integrating the data pipeline and outputs of machine learning models into an easy to use management platform.

_Data Science_: Data science is central to our predictive Quality, Process, and Market Intelligence services. We didn’t build a data science team to optimize our product's marketing spend, sales funnel, or client retention – we built a data science team to build our product. We need data scientists who can understand our clients and can take a nebulous business goal, create a set of quantitative decision metrics, and build predictive models to optimize those metrics.

The extensive role of data scientists at Analytical Flavor Systems allows us to invest in their education across sensory perception (standard sensory science so they know what we’re improving and replacing), tasting experiences (so they appreciate the products we work on and understand how the data is collected), production knowledge (test batches in our R&D brewery and roastery so they understand the data they work with and how our predictions impact a client’s process), and data science tear-downs (a meeting where the team collaboratively attempts to find and fix problems, try new techniques, and debate the philosophical implications of a model's construction).

_Sales_: We prefer the thoughtful relationship builder to the cowboy negotiator. Most of our contracts are multi-year high-price affairs, so relationships are really important. Plus, you get to spend your time at breweries, distilleries and roasteries! (I've personally never been to a sales meeting where beer or coffee wasn't served freshly brewed)

Next Steps

Please submit something awesome to JasonCEO@Gastrograph.com to apply.

I'm the author of this post and the CEO of Analytical Flavor Systems - I'm happy to answer any questions!

You should consider applying for a full time position ;)

That's now how percentages work....

It's a 3 month paid internship, and 99% of the students have been from Princeton.

Did you see the work we linked to? That's intern work here - we treat our interns as full members of the team, and they've delivered.

On this note: My team and I at Analytical Flavor Systems[1] wrote a blog post on how we go about hiring data science interns[2]. It's heavier on the technical details, and suffers from less... romanticism....

[1] www.gastrograph.com

[2] https://gastrograph.com/blogs/gastronexus/interviewing-data-...

As a potential candidate, all of the standard complaints ring true - but once you're on the other side of the equation, and need to hire people... your ability to create new interviews is not nearly as wide or as clear as it would seem from the outside.

1) Take home test: OK for performance metrics, bad for "getting to know" the candidate, and terrible for selling the candidate on your company

2) Daylong interview: Expensive, requires interrupting our team, needs a fully planned and well executed itinerary - but is perfect for getting to know someone, getting the feel for their personality and interests, and is the best way to sell someone on the opportunity.

3) Work sample: we usually do this for interns[1] and pair it with a ~1 hour conversation (either before or after, doesn't really matter to us) on what the company is like and what they would be working on. Obviously, work samples suffer from the same deficiencies as a take home test for cultural fit and the like, but it's the best we can do for interns!

[1] https://gastrograph.com/blogs/gastronexus/interviewing-data-...

I disagree on multiple fronts!

1) ggplot does exactly what it is supposed to do: create data visualizations. It made no promises for interactivity or display, and in fact, it was originally designed for creating publication quality charts, which it continues to do well.

1.5) ggvis is a D3 API wrapper on ggplot and allows for interactive graphics. Do you want to pay your data scientists for creating production ready graphics or let them focus on what they're best at?

2) R has been growing - outside of neural networks (which R needs to catch up on), R gets almost every pre-processing and modeling algorithm first, and distributes it for free. Furthermore, it has better sampling options, metric options, augmentation options, and model ensabling tools (stack or meta-model) VS any other language or framework - it is the gold standard.

3) I don't think there's any "magic" in R. It's just a language with a learning curve and lack of opinions.

4) Last point: R is really not built for the web (it's older than Python!) - its built for data science. There's no reason you need to run your modeling stack in the same language as your application server. R is perfectly capable of writing to databases or sending API responces in JSON or PMML.

/endrant

Not trying to start a flame-war - but this type of difference in opinion is important to see when thinking about hiring data scientists or deploying models.

To contrast this - I'm the lead data scientist at the same company, and head over heels in love with R....

It is the only language I can quickly and efficiently jump from algebraic topology for novel pre-processing, straight into model building and validation - with just about every potential variation of every major algorithm freely available and packaged on a well curated package manager (CRAN), and then ensemble them.

I _agree_ that it's a bit difficult to use in production, and that dependency management needs work (Packrat is trying to do that), and that blindly trusting packages on CRAN can cause errors - but 98% of the time - it just works. Graphics, models, crazy niche things that are currently only used by one post-doc locked away in a top secret research lab... it all just works.

Of course, take this with a grain of salt: this is coming from a guy who's built web-servers (HTTP responses and all) in R.

I'm Jason, the CEO and lead data-scientist (for now) at Analytical Flavor Systems. Ask me anything!

Phabricator for task managment, project management, and code reviews. It's open source, great software, and has a fun personality great for internal use.

At Analytical Flavor Systems[0], we have a ridiculous number of traditions... and as Founder/CEO, I have nearly no control over them!

A select subset of our traditions include:

- Daily: 24 hour goals (what did you accomplish in the last 24 hours? what do you plan to accomplish in the next 24 hours?) every morning as a standing meeting, with pour-over coffee (usually client, sometimes... almost clients). We always discuss our analysis of the coffee and the brewer/barista (which rotates through employees) after 24 hour goals.

- Daily: Highs and Lows. What was the best part of your day? What was the worst part of your day? (confusingly, we start with the low, and end on the high note).

- Monday: we have a modified version of the Rebeca Black Friday song... about how much we like to work/twerk on Mondays... This was created, died, and revived a number of ties has the team has grown and changed.

- Friday: Beverage Exchange: We don't hold official panel tastings on Friday's, out of respect for people who have lives outside of the office (this is totally theoretical) - so we exchange and share rare and interesting products we've collected. Considering the company is building AI for the beer, coffee, spirit, and wine industries... we have access to a lot of rare products to share and taste outside of official panel tastings!

- Hazing of new employees: It takes a long time to become an able barista (coffee tastings during 24 hour goals) or capable beer/wine taster. We're very upfront about how much new employees suck until they get it - experience and trust scores are read out, deviations in perceived quality due to brewing skill is listed, and missing variables that needed to be interpolated are explained (in excruciating detail).

Clearly all of these traditions won't continue as we move from 12 to ~40 employees over the next ~18 months... but the important thing is that we've set ourselves up with a strong culture that cares about our clients and cares about the same things our clients care about (these two topics are very different!)

The best piece of advice that I have for other founders: create the seeds for traditions to form, but allow the employees to decide which to water and cultivate - allow them to decide which traditions get proginated and carried forward from generation to generation. And if possible, record past traditions in your Phabricator[2] Wiki.

[1] www.Gastrograph.com [2] http://phabricator.org/

Growing up in a ultra-reformed Jewish-by-culture family, I have grown to truly hate Christmas music... the overt display of cultural dominance paired with the unapologetic consumerism irks me - I find it oppressive and evangelizing but totally un-inviting.

That's a personal question, isn't it? :)

More seriously, it's not always a choice - I got into it because a few products hit me and I said "WOW, s/coffee|chocolate|beer|wine|tea can taste like that?".

It's Pandora box and I'll never be satisfied knowing better is out there now!

My other comment above is a better reply to this:

> Quality is a tricky word, but in terms of the appreciation of craft goods, your ability to identify the subtleties that make more higher quality products _better_ is a learned trait.

If your references are wrong, or you miss out on the identification of a nuance, you haven't learned from the experience or you've learned bad information.

For example, individuals who say "I can't taste the difference between a $30 and $300 bottle of wine" are not _wrong_ - they just lack the formative experiences and education to appreciate the attributes that the more expensive product has.

There is an entire world of great flavor out there that is very hard to perceive; for example, I don't eat any artificial flavorings, preservatives, or food coloring, and when actually doing tastings for work, there are lists of black list foods and cut-off times for a clean palate - we go to that extent because anything less would cause us to miss what makes that product unique and special - we would be entirely unable to perceive it, learn from it, and appreciate it.

Quality is a tricky word, but in terms of the appreciation of craft goods, your ability to identify the subtleties that make more higher quality products _better_ is a learned trait.

If your references are wrong, or you miss out on the identification of a nuance, you haven't learned from the experience.

For example, individuals who say "I can't taste the difference between a $30 and $300 bottle of wine" are not _wrong_ - they just lack the formative experiences and education to appreciate the attributes that the more expensive product has.

Thus, if you decide that wine or beer or chocolate was of interest to you, it may be worthwhile to validate and trust your reference products.

Obviously I disagree with this; the appreciation of many products take some level of experience to understand what subtilty and nuance to look for.

If you we're to use Mast Brothers chocolate as your "high quality" reference, you would be learning to identify the wrong attributes as indicators of quality.