jakek

Summary of the ruling with annotations (written & audio): https://guidedsearch.ai/results?q=https%3A%2F%2Fwww.cit.usco...

Founder of Insight here (YC W11). Since 2012 we've been running free fellowships to help PhDs transition to roles in data science [1] and more recently health data [2]. Similarly, since 2014, we have been helping professional software engineers learn and move into data engineering roles [3]. Finally, since 2016, we have been helping researchers and engineers move into top applied AI teams working on cutting edge products [4]. Over 1200 Insight alums now work on data teams at 300+ companies.

For the past year, due to the growth in applied AI / deep learning teams in industry, many top teams are now hiring product people to lead teams of data scientists, AI engineers, etc. We’re also increasingly receiving applications from product managers who have experience building data-driven products and want to build sophisticated products during their time at Insight. The new Insight Data PM [5] will take an existing PMs experience and help layer on the AI / ML / analytics piece needed, with Fellows interviewing for Data PM roles at top companies immediately after the fellowship.

[1] Data Science: http://insightdatascience.com [2] Health Data: http://insighthealthdata.com [3] Data Engineering: http://insightdataengineering.com [4] Artificial Intelligence: http://insightdata.ai [5] Data Product Management: http://insightdatapm.com

This is fantastic news for entrepreneurs everywhere.

Garry has been instrumental to the success of my company from the earliest days when we were back in YC W11 through to the present day - I'm so excited for all the new founders who will get an opportunity to work with him as a result of this new fund. Garry is not only an extremely kind person, extremely smart,but also completely understands what it means to have ups and downs as a startup since he's been there and helped hundreds of other companies through those ups and downs. So when you need someone in your corner he's there every step of the way. If you're an early stage founder I couldn't recommend Initialized and Garry more highly - super excited for what Garry and team are going to build.

Yes, absolutely, one of the goals of the program is to help researchers with strong quantitative backgrounds and experience in machine learning enter the industry, despite not having a background traditionally associated with ML.

Since the New York session is a bit further out, we're still finalizing the mentor companies. That said, for our NYC data science and engineering programs, companies hiring Insight Fellows include Facebook, Bloomberg, Capital One, New York Times, Memorial Sloan Kettering Cancer Center, and dozens of others. We expect many of these companies plus other NY AI teams to be actively participating as we have already received significant interest.

The litmus test for me on whether we're adding adding value as an education company has always been: are there Insight Fellows who get rejected from companies X,Y,Z prior to Insight then get offers from X,Y,Z after Insight? From the very first session through to today, we have numerous examples each session of this happening.

A recent example was a Data Science Fellow who was a physics postdoc at Lawrence Berkeley National Lab prior to Insight. Right after his postdoc ended, he applied to half a dozen bay area tech companies (all the usual brand name suspects), got rejected from all of them. He came to Insight and during his fellowship built a video scene segmentation & object detection project with a YC startup. After Insight he got an offer from every one of the companies he previously got rejected from. He went on to accept an offer on the LinkedIn data science security team (which is led by another Insight alum).

We’ve seen this happen time and again on the software engineering side as well with our data engineering program. A Data Engineering Fellow prior to Insight has a generalist software engineer experience but a passion for big data, wants to do big data full-time, but no one will take a chance on her/him. At Insight they build a sophisticated data pipeline on AWS, while being mentored by leading data engineers, and then the same companies previously rejecting that Fellow for data engineering roles make offers because they now have the evidence they need that she/he can solve the types of specialized problems the company is facing.

For the data science (DS) program we’re very open to strong quantitative fundamentals without ML experience, as many of the DS roles are focus on analysis. For the AI program, since the focus will be entirely on getting you up to cutting edge of what’s happening in ML, it will be important to already have had experience with the fundamentals of ML. Luckily, for someone with your background, it’s completely possible to build some impressive side projects in a couple of months than can demonstrate your ability to learn ML at a rapid pace. We’re far more interested in how quickly you can learn than your current level of knowledge, so doing a side project and mentioning your starting point prior to the projects will give us strong signal that if we accept you into the fellowship you’ll be able to pick up the necessary techniques.

As an aside: it’s astounding how quickly Fellows with strong quantitative backgrounds can learn when surrounded by other really great people. We’ve had numerous mathematicians and theoretical physicists at Insight who barely touched any data in their PhDs, go on to build sophisticated machine learning data products at Insight and get hired at top tier data science and machine learning teams.

Regarding the location: the initial programs will focus on roles in the SF Bay Area and New York, so we’d like to attract Fellows who are interested in living in either of those respective cities. That said, our network has grown well beyond those cities so I encourage you to apply regardless of geographic preference and just let us know in the application where you hope to end up after the program.

Founder of Insight here (YC W11). Since 2012 we've been running free fellowships to help PhDs transition to roles in data science [1] and more recently health data [2]. Similarly, since 2014, we have been helping professional software engineers learn and move into data engineering roles [3]. Over 750 Insight alums now work as data scientists & engineers at 200+ companies.

This past year, we've seen more highly specialized applied AI / deep learning roles emerge in the industry. We're also increasingly receiving applications from scientists and engineers who have some machine learning experience and are learning to build out sophisticated deep learning models during their time at Insight. The new Insight AI [4] program will focus on allowing Fellows with these backgrounds implement the latest ML techniques from research or contribute to open source projects under the guidance of industry leaders, then join AI teams in Silicon Valley and New York after the program. Insight AI will accept both software engineers and quantitative scientists (no PhD required).

[1] Data Science: http://insightdatascience.com

[2] Health Data: http://insighthealthdata.com

[3] Data Engineering: http://insightdataengineering.com

[4] Artificial Intelligence: http://insightdata.ai

No paywall link: http://bit.ly/1yh5c0D

https://static.googleusercontent.com/media/research.google.c...

The reason for this is that the Insight Data Science Fellows Program is designed for (and only accept applications from) PhDs/postdocs. The new stand-alone Data Engineering program (http://insightdataengineering.com) is open to all applicants, as long as they have good engineering/CS fundamentals, regardless of level of education or discipline.

Great to hear. We love skills learned on-the-job. That list is just meant to cast a wide net, so people feel welcome to apply from various backgrounds. The main take-away is that we want people who have the right fundamental skill set, and are not too concerned about which formal discipline they learned it under.

The Insight Data Science Fellows Program is currently for PhDs only. If you have enough engineering experience, I would suggest applying to the Data Engineering program, which is open to anyone. If not, then drop me a line at jake@insightdatascience.com and I can see what I can do to help.

The entire program is based entirely around professional data engineers from the mentor companies coming in to share their best practices with the group, which the Fellows than work to implement in their projects. A number of mentors have told me they will focus on the topics you mentioned. That said I would love to get your take on this too. Would love it if you drop me a line at jake@insightdataengineering.com with any suggestions. Thanks!

For anyone who applies before the end of this weekend, we'll be in touch by mid-week next week with decisions on next steps in the process. Final decisions should be made about 1.5-2 weeks after that. We'll move equally quickly for applications that come in next week through to the April 14 deadline.

Thanks!

If you haven't already, I would recommend taking an intro to databases course. A machine learning course would also be helpful, if you have time to take it before you finish. Other than courses, I would try to build some weekend projects that demonstrate your ability to write clean, modular code.

Regarding hours: Insight is really intense, so while the official hours during the six week program are M-F 10am-6pm, most Fellows stick around pretty late each evening. The peer-to-peer learning aspect of the program is one of its biggest strengths, and you get the most out of that when you can be around the office collaborating with others as much as possible.

I'm the founder of the Insight Data Science Fellows Program, and the new Insight Data Engineering Fellows Program we just launched above. With the Data Science program, which helps PhDs transition to industry, we're at 70+ alumni working as data scientists at companies like Facebook, Square, LinkedIn, Airbnb, etc.

This new Data Engineering Program is NOT restricted to PhDs, and open to all professional engineers or BS/MS graduates. It's still free, just like the Data Science Program, and is designed for people who want to leverage their existing software engineering skills to transition to a career in data.

Happy to answer any questions here.

You failing is part of your investors business model. They've moved on, they no longer care.

This sounds like a negative statement, but it actually has extremely positive repercussion: you no longer have to worry about them AND you have a few months to do whatever you want. Anything. Have fun and enjoy it. Maybe build something cool, completely new, something that you want to see exist in the world. And you never know, just letting go of the burden, accepting that what's done is done, and enjoying creating something for fun may actually lead to something.

See: http://www.physics.ohio-state.edu/~kilcup/262/feynman.html

Fantastic story. I would love to hear some first-hand accounts / comments from any Kenyans on HN. Is there anyone here currently in Kenya and involved in startups?

Check out: UI Stencils http://www.uistencils.com

Can't wait to try POP and UI Stencils together.

Fantastic post - thanks for this!

Any thoughts on how to potentially apply these ideas to writing actual code?

For instance: imagine if there was an IDE for python that had the 'look & feel' of FreeMaps. Then, perhaps, you would effectively gain many of the same benefits applied directly to writing the program (ie: very dense; being able to see forest and the trees).

Agreed. I've been looking at ways of communicating complex science-based or data-driven insights in compelling ways, and I think easily digestible narratives, like this one, are a great way to get broad range of people interested. I would love to ask you some questions and get your advice on how to better do this. If you're interested, please drop me an email: jake at insightdatascience.com.

Here's a helpful mental hack that I've found has increased my grit as an entrepreneur:

I am very optimistic that I will be successful in the long term, but very pessimistic about the odds that the immediate next step will come easily or go well. This way, when a step proves difficult (and it very often does), it's not unexpected, making easier to keep moving forward without getting discouraged.

plessthanpoint5, I'm in the process of doing some research on techniques for learning software development online. Any chance I could email you with some questions? I'm at jake@noteleaf.com.

We've used WePay to split bills between three roommates for over a year now - it's saves us a huge amount of hassle each month

From http://news.ycombinator.com/item?id=1533796 :

The Mountain View Hacker House has a room opening up August 15th, if you're interested (or know someone interested) please contact jake@noteleaf.com. Thanks!

You're absolutely right, being in silicon valley does not, by itself, equal success. There are other factors that are far more important and there is no silver bullet. That's why there are tons of examples of great startups all over the world, including ones based right in my hometown.

So, if you personally have no desire to move here and are happy with your startup right where it is, then you should stay where you are.

All that being said, if deep down you've got the feeling that the valley might be the place for you, you see that you're not moving forward on your startup as fast as you'd like, or you haven't yet found the right people to work with, then it's a move I highly recommend.

On a related note:

"Lunar Cycle Effects in Stock Returns" http://papers.ssrn.com/sol3/papers.cfm?abstract_id=281665

Abstract: "We find strong lunar cycle effects in stock returns. Specifically, returns in the 15 days around new moon dates are about double the returns in the 15 days around full moon dates. This pattern of returns is pervasive; we find it for all major U.S. stock indexes over the last 100 years and for nearly all major stock indexes of 24 other countries over the last 30 years."