The Data Incubator is an intensive 8 week fellowship that prepares the best scientists and engineers with advanced degrees to work as data scientists and quants. It identifies fellows who already have the 90% difficult-to-learn skills and equips them with the last 10%: the tools and technology stack that make them self-sufficient, productive contributors. The program is free for fellows. Employers only pay a tuition fee if they successfully hire.
New Fellows have the option to participate in the program either in person in New York City, Washington DC, San Francisco Bay Area, or online.
Training that links your analytical skills to job opportunities
Employer-paid Scholars keep the program free for admitted Fellows
Learn from senior data scientists at our hiring companies and build your professional network
Opportunities with the most innovative employers in technology, healthcare, and finance with $100K - $200K starting compensation
Make the transition from academia with a selective peer group excited to learn and collaborate
Apply the tools that employers value to real-world datasets. All powered by a 100-node cluster
Data Scientist: The Sexiest Job
of the 21st Century
HARVARD BUSINESS REVIEW
Michael has worked as a data scientist (Foursquare), quant (D.E. Shaw, J.P. Morgan), and a rocket scientist (NASA). He did his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall scholar.
At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup that lets him focus on what he really loves.
Cofounder and Head of Research, Quantitative Brokers
Senior Data Scientist, Truveris Health
Director, Data Analyst Team at Etsy
Data are becoming the new raw material of business
Become a hiring partner! It’s free to see resumes, review code, examine capstone projects, attend events, meet the Fellows, and conduct interviews. There is only a fee for Fellows you hire.
Meet the brightest minds leaving academia who aren't yet on the market. We accept fewer than 5% of our advanced-degree applicants
Introduce your company to the next generation of data scientists. Get to know your peers at our other dynamic hiring companies
Our Fellows have been sourced, screened, and trained by top industry data scientists, reducing hiring time and on-the-job training
Have openings right now? Not hiring until next year? Our program graduates four cohorts of fellows per year, so we're here when you need us
Enroll your employees in our fellowship program to boost their data science skills.
Leverage world-class industry expertise to train your budding data analysts into rockstars. Learn the latest techniques and technologies from machine learning to hadoop
Have a promising data analyst you'd like to hold on to? Enroll your rising star in our dynamic fellowship and inspire them for more
Train and leverage existing talent, reduce reliance on outside vendors, and keep data secure within your organization
Enroll your data managers in our program and develop leaders who will disseminate these new best practices in your organization
140,000 – 190,000 more deep analytical
talent positions needed
McKinsey Global Institute
The next program (both in-person and online) will be 03/21/2016 – 05/13/2016.
Sign up here to get information about future sessions.
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With loads of data you will find relationships that aren’t real.
Big data isn’t about bits,
it’s about talent.
Ninety percent of all the world's data was generated in the last two years. Every 2 days, we generate as much data as all of humanity did up to 2003. Data scientists have the analytical and programming skills needed to extract valuable knowledge out of the data. The unique combination of skills that data scientists have is used across many industries for projects such as:
The demand for data scientists is growing exponentially [1, 2] and McKinsey estimates a need for 140,000-190,000 more data scientists over the next few years . New York City, with a burgeoning technology sector and home to more Fortune 500 companies than anywhere else in the world, is quickly becoming the center for data science. The competition for talent has led to compensation packages for talented first-year data scientists in the range from $100K to $200K.
 "Big Data Needs Data Scientists or Quants" (Forbes Magazine, 2012)
 "What Are The Odds That Stats Would Be This Popular?" (New York Times, 2012)
 "Big data: The next frontier for innovation, competition, and productivity" (McKinsey Global Institute, 2011)
There are four main components to the program:
Bootcamp modules. Short modules covering both the technical and non-technical skills necessary to succeed in industry.
Seminars with mentor data scientists. Unlike academic research seminars, we promise these will actually make sense. Hear from the top data scientists in the world about what data science is like for them.
Build a series of mini projects to showcase your programming and mathematical talents. Hone your skills on a 100 node cluster and get hands-on experience with real-world datasets.
Interview with amazing employers. Meet employers looking for top applicants.
The program builds on your scientific training and provides you the skills needed to quickly have large industry impact. While an advanced degree is excellent preparation, our experience has shown that academic researchers often lack a few key skills. The curriculum includes:
Software engineering and numerical computation. Numerical techniques for optimization and vectorized linear algebra. Programming tools including python, numpy, scipy, scikit-learn, matplotlib.
Natural language processing. Handling unstructured data, stemming, bag of words, TF/IDF, topic modeling.
Statistics. Hypothesis testing, regression and classification, ensemble methods, cross-validation, variance-bias decomposition, data normalization.
Data visualization. Including geographical and temporal data. Packages like d3, ggplot, matplotlib.
Databases and parallelization. SQL, Hadoop, MapReduce, Hive, Spark.
Succeeding in industry is as much about soft-skills as technical ones. We cover some of the basics:
Communication skills. Academics and people in industry communicate in very different ways. We'll work with you to avoid common pitfalls and distill your research and data science insights into messages that will be appreciated by non-experts.
Networking. Meeting people is really important for your career but there are half a dozen subtle mistakes that young professionals frequently make. We'll help you avoid them.
Practice interviews. Technical interviews can be notoriously tough. We help our Fellows prepare so that they know what to expect.
Leverage your degree. Training that links your analytical skills to job opportunities.
Free Tuition for Fellows. Employer-paid Scholars keep the program free for admitted Fellows.
Mentorship from hiring firms. Learn from senior data scientists at our hiring companies and build your professional network.
Jumpstart your career. Opportunities with the most innovative employers in technology, healthcare, and finance with $100K - $200K starting compensation.
Smart, passionate Fellows. Make the transition from academia with a selective peer group excited to learn and collaborate.
Build a series of miniprojects. Apply the tools that employers value to real-world datasets. All powered by a 100-node cluster.
The program is in partnership with the Fellows and while we provide our Fellows with a lot, a few things are expected in return:
Make a commitment to be a part of the program. For the in-person program, this means moving to New York City, Washington DC, or San Francisco Bay Area for the duration of the entire program and being there every day during the workweek, interacting with the other Fellows, and working on your portfolio project. You should really think of this as a sort of internship. For the online program, this means setting aside enough time to participate remotely part-time. It is difficult but possible to do so while working full-time.
While it is not required, we highly recommend you stay two extra weeks for optional advanced topics as you interview for jobs. These additional weeks were added based on near unanimous request from previous Fellows.
Make a commitment to work as a data scientist in industry shortly after completing the program. We ask that you interview with our hiring companies immediately after the program. If there's another company that you would like to interview with, just notify us in advance so that we have a chance to work with them as a hiring company. Most employers would prefer you start within 2-3 months of an offer.
Decline to work with external recruiters while in the program. We provide training to Fellows for free and compete with external recruiters who charge for just making a placement without providing any training. Working with them prevents us from investing in curriculum and improving the program for future Fellows.
We welcome applications from anyone who has or is within 1 year of receiving their master's or PhD from any math, science, engineering, or social science field, including math, physics, chemistry, biology, psychology, social science, operations research, neuroscience, and many others. This includes postdocs, faculty, master's, and PhD candidates about to graduate, and people who already have a master's or PhD. The program is geared towards helping them make a transition to the private sector from academia and we are looking for candidates who want to start within 12 months of completing the fellowship.
Absolutely! Please sign up here.
Absolutely! People with industry experience are often some of our strongest candidates. Please sign up here.
Absolutely! There are no formal skill requirements for the program. That said, most applicants have some familiarity with programming.
Absolutely! We encourage applicants from all countries to apply. You can participate in the program through any visa that allows you to be in the country and take meetings, e.g. a visa that would enable you to participate in a U.S. conference.
We are looking for people with strong scientific training who are able to work with data programmatically and can make valid real-world inferences based on data. Applicants usually have a strong background in probability, statistics, and experience with programming, scripting, or statistical packages.