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.
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.
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.
In your experience, how much of the interviews for an AI / DL role consists of classic CS algorithm puzzlers, compared to a regular software engineering interview at a place like Google / Facebook?
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).
I actually pretty interested in joining this program since I am near one of the locations! Do you guys accept other type of engineer (in my particular case Mechanical Engineering) with a background in programming?
Engineers often have a lot of experience with control theory and building maintainable systems, and individuals who have a strong math and coding background would be a great fit. Side projects in deep learning and reinforcement learning would also be a good thing to highlight when applying.
For the AI program, what kind of ML experience are you looking for?
I'm a physics PhD (working in ultrafast atomic molecular and optical physics theory), finishing up in the next few months, and I would love to transition into AI as a field. Unfortunately, I don't have an extensive portfolio of ML projects. Is this something I should beef up before I apply?
I understand you are likely much more comfortable with taking people interested in SV and NY, but for those in other areas (Denver, for example) and unwilling to relocate, is this going to kill our chances of being accepted?
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.
Do you require that fellows to accept a job with one of the Mentor companies after the program?
The program looks really interesting and I'd definitely like to increase my skills in this area, but it's also unlikely that I would accept a job at a new company. (I would be happy to interview though - maybe a company could convince me.)
Also, I would suggest putting the important information in something besides a "white paper," like a FAQ.
Very cool! I've worked with the Insight Health Data fellows before, enjoyed the experience, and they got a lot done.
I think one thing that's tricky about artificial intelligence as a field is that it involves so many diverse pre-requisites. For example, although Caffe lets you configure a DNN with just a simple text file, when something goes wrong, the stack traces are all in C++. The abstractions tend to leak.
I know that both software engineers and quantitative scientists are encouraged to apply. On the quantitative scientist side, what level of programming ability do you think somebody needs to succeed in the program?
Likewise, on the software engineer side, what level of mathematical background would you expect someone to have coming in?
The quantitative researchers, in addition to having advanced machine learning experience, should have a strong coding background - experience with large code bases and best practices in software development. Many physicists, computational biologists, neuroscientists, etc come from this background, having worked in collaborations and implementing machine learning methods on messy real-world data collected from large experimental setups. We’ve had Fellows like this, who also in their spare time built and trained networks in TensorFlow and then built their own customized layer in C++ behind the scenes.
The software engineers coming into the program would have machine learning experience, but have not necessarily in a full-time role yet. Just like the software engineers that come into our data engineering program, it’s a chicken or the egg problem: employers want to see experience in the role before hiring for it, but how can you get experience if no one wants to take a chance on hiring you. Insight takes that chance, you work on cutting edge ML problems here, then the company has evidence (obviously combined with your previous years of work experience) to than make a bet to bring you on as an ML engineer.
Overall AI practitioners in these roles usually fall along a spectrum, having their main strength be either software engineering or quantitative research. Often companies will have experts on both ends work together to implement current research and then put those models into production.
I lead the team running the new Insight AI fellows program in Silicon Valley. For the past year, I've been providing guidance and mentorship for Insight Fellows doing deep learning projects as part of our data science program. My background is in applying current deep learning research to large scale audio and video data sets to understand animal behavior and cognition. I'm happy to answer any questions.
Hi, for PhD students, when is the recommended time for applying to this program? For the data science program, I believe it is 1-3 months before degree completion; is it the same case for the AI program? More specifically, I'm on track to graduate in 2018, but interested in looking for internships/bootcamps during summer 2017.
You need to be able to start your new full-time AI role within about 2-3 months of completing Insight. So we recommend applying within the last few months of your PhD, which will allow for a smooth transition from your program into your new role.
This is a very interesting program and I imagine there will be lots of applicants. So this leads to my question, how many fellows will be accepted to each session? Also, for those of us who see this as a life-changing opportunity, what would you suggest we do to improve our chances of getting accepted and successfully completing the program?
Hi, would you consider graduates from non-CS fields? I will very soon have a PhD in Civil Engineering where I do statistics and Machine learning for solving weather-related problems. Have good experience with R and Python.
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.
Doesn't this further prove my point? If you're saying that some tasks, like NLP, are too complex to tame and you should just throw more data at it, you're basically capitulating to complexity and taking the easier route. Isn't that the opposite of what researchers should be doing?
There's no shame in taking a non-optimal path, and then working out how to get better at it later. Research is about solving problems, answering questions. Why should that have to be the hard way?
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.
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.
Do you teach people about "hygiene", e.g. data provenance, versioning, and how to design schemas? I work on "big data" stuff at one of these major companies, and IMO the state of things is pretty sad. A typical pipeline involves a bunch of files strewn about a distributed file system, or a pretty messy database, especially when multiple teams are involved.
The tools (I use) don't encourage good practices or have good defaults. You have to put in extra effort and write proper metadata, etc.
I think things are just new so this kind of issue doesn't get much attention yet. Curious to see if anyone has written anything about it. I guess academics and government and people who have to keep data around for a long period of time will have thought more about this.
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!
Wow this is really awesome and I'd like to participate so I'll ask a couple questions.
I'm in my last semester of university for CS (starting graduate school in the Fall), what kinds of 'pre-reqs' would you suggest in terms of languages/programming paradigms/statistics knowledge?
What is the weekly number of hours we should be prepared to commit?
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 see that the application deadline is April 14 for a program starting June 2. When do the accepted candidates get notified? This would be pretty important for an applicant outside of the bay area.
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.
I'm seriously thinking about applying (actually to the Data Science program). I'm currently looking to start in the SF Bay Area as a data scientist (or data analyst, if need be) in late May to June.
But I have a question -- I've only advanced to the masters level at this point; I recently graduated with an MS in biostatistics. "PhD" and "postdoc" is written all over the site. Should I even consider applying?
Finally -- what's the best way to contact you? Should I email the email address under "Contact"? Or is there a preferred alternative?
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.
Thanks for the response; I'm definitely applying to the Data Engineers program. My engineering background has been more on-the-job than from formal coursework, so I was a bit subdued by list of engineering disciplines in the "Accepting Applicants From" section (though I now see "Scientific Research" as one of the fields, yay!). I really hope I can participate.
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.
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.
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