Step 1: Complete the application for a cohort location.
Step 2: Work on a challenge problem and submit your solution.
Step 3: Candidates selected to move forward will be invited to schedule a 45-minute interview with a mentor or a former fellow.
Step 4: Candidates selected to move forward will be invited to schedule a 20-minute call with a member of the leadership team.
Step 5: Selected candidates will be sent an offer via email.
Join us for an upcoming Ask me Anything session. Please read the FAQs below first!
We follow a trimester system that divides the year into three terms of 14-16 weeks each. Here are important deadlines to be aware of:
JAN 7 - APR 30, 2019
Oct 1: Rolling admission
Nov 26: Deadline for applications
Dec 3: Challenge deadline
Dec 10: Interview deadline
Dec 14: Final notifications
MAY 1 - AUG 31, 2019
Feb 1: Rolling admission
Mar 29: Deadline for applications
Apr 8: Challenge deadline
Apr 19: Interview deadline
Apr 22: Final notifications
Jan 6 - Apr 30, 2020
Oct 1: Rolling admission
Nov 30: Deadline for applications
Dec 3: Challenge deadline
Dec 14: Interview deadline
Dec 24: Final notifications
SEP 2 - DEC 20, 2019
Jun 1: Rolling admission
Jul 31: Deadline for applications
Aug 13: Challenge deadline
Aug 24: Interview deadline
Aug 26: Final notifications
Frequently Asked Questions
For questions related specifically to the challenge problems, take a look at the FAQ section on the challenge page.
Program Basics & Application PRocess
How long is the program?
The program is 4 months on a full-time basis. We do not currently offer a part-time option.
How much does it cost?
The program is free to the fellows.
Can the fellowship program be done remotely?
A key aspect of the learning is the in-person communication with mentors and other fellows. We don't believe the same level of collaboration is possible remotely so we currently do not offer this option.
Do you sponsor visas?
Currently we do not have the ability to sponsor visas.
The program is offered in expensive cities, do you offer any stipend or living accommodations?
At this point we don't offer any assistance.
What are my chances of getting in?
Our acceptance rate is currently 6% of all applicants. Candidates who perform well on their challenge exercise and make it evident that they have put thoughtful time and effort into it have the highest chance of acceptance.
What prior knowledge is required to succeed in the program?
The challenge problems will give you an idea of what is expected from successful candidates. We look for creative problem solving ability, basic coding proficiency (particularly in Python) and a foundational understanding of machine learning theory and methods.
What do you look for in candidates?
We do not have any specific education or work experience requirements, as we believe that great data scientists and AI practitioners can come from any area of expertise. For this reason, we put much more weight on your demonstrated ability evidenced by your challenge submission and interview performance than we do on your credentials.
We highly encourage applications from candidates in groups underrepresented in AI, whether in terms of gender or gender identity, sexual orientation, ethnicity, age, educational background, or career path.
What can I expect in the interviews?
The first interview, depending on your skills demonstrated in your application and challenge submission, will involve some or all of the following:
Motivation and why you are interested in the fellowship
You challenge solution and the reasoning behind it
Your knowledge of machine learning concepts
Your coding skills (in Python)
Your general data science skills, such as feature engineering
The second call will be a more informal conversation that will give the fellowship leadership the chance to get to know you better, and give you another chance to ask questions.
Why this Fellowship?
How is this program different from other data science programs?
The fellows work on actual machine learning products that are used in production environments. Fellows work under the supervision of the mentor team. Mentors are actively involved in the delivery of projects, including coding.
Fellows also have an opportunity to interact directly with our customers and get immediate feedback on their results.
What happens to fellows after they graduate? What jobs do they get?
Our fellows are now in machine learning roles at Uber ATC, Facebook, Google, Sentient Technologies, Yelp, Orange, Pivotal, etc.
We also offer paid externships through our commercial arm, Launchpad.AI.
What type of projects will I get a chance to work on?
We apply deep learning and large-scale optimization expertise to variety of industry problems. Most of our projects involve deep learning and reinforcement learning on large data sets.
What does the day-to-day look like?
The majority of the time is spent pair programming. We pair up a fellow more proficient in quantitative skills with a fellow more proficient in software development. The project team typically consists of two fellows working under supervision of a mentor.
We have daily scrums, and we are very diligent about it. We have internal Slack channels, shared GitHub repos and Trello boards. We have a weekly retrospective and iteration planning.
What tools will I get a chance to learn?
We are primarily a Python shop but fellows are free to use whatever tool and technique they believe is best suited to the problem. We typically use a variety of machine learning libraries including TensorFlow, Keras, PyTorch, and sci-kit learn.
What percentage of the fellowship is actual model building?
Model building is an iterative process. Typically, we spend 50% on data wrangling, 40% on modeling, and the remaining time on explaining results to business people.