The Data Incubator is an intensive six-week fellowship that prepares the best science and engineering PhDs 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. Fellows have the option to participate in the program either in person in NYC or electronically.
Training that links your analytical skills to job opportunities
Tuition is 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
Showcase your knowledge by applying the tools that employers value to proprietary industry data
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 let 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
Join us as 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 some of the brightest minds coming out of academia. We accept fewer than 5% of our PhD applicants.
Interview data scientists who aren't yet on the market. All have committed to leave academia and start working in the private sector.
Introduce your company to the next generation of data scientists
Our Fellows have been sourced, screened, and trained by top industry data scientists, reducing hiring time and on-the-job training
Throughout the program, you'll have several opportunities to engage with our Fellows. These additional insights lead to better hiring decisions
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
140,000 – 190,000 more deep analytical
talent positions needed
McKinsey Global Institute
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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 the data. The unique combination of skills that data scientists have are 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. These are not mandatory so just attend the ones you want.
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 portfolio project to showcase your programming and mathematical talents. Employers are naturally skeptical and it's way better to show than to tell. We'll guide you through choosing and building a project using the skills and techniques that they care about.
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 a PhD or postdoc 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 PhD. Training that links your analytical skills to job opportunities.
Employer paid scholarships. Tuition is 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 PhD Fellows. Make the transition from academia with a selective peer group excited to learn and collaborate.
Build a portfolio project. Showcase your knowledge by applying the tools that employers value to proprietary industry data.
The program is in partnership with the fellows and while we provide our fellows with a lot, a few things are expected in return:
We welcome applications from anyone who has or within 1 year of receiving their 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, PhD candidates about to graduate, and people with PhDs in industry. 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.
Please request an application here to get on our email list. While we are only taking applications from PhDs at this time, we are looking to expand the program to masters students shortly.
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 programatically 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.