Frequently Asked Questions
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.
Is the fellowship program in person or remote?
Yes, the program is 100% remote. We encourage fellows living in major metropolitan areas to meet in-person at least once a week.
Do you sponsor visas?
Currently we do not have the ability to sponsor visas.
Can I get in if I don’t submit a challenge?
You will be given priority consideration if you submit a challenge, and your chances of being accepted will significantly improve. We occasionally waive the requirement for exceptional candidates.
Do you offer any stipend or living accommodations?
At this point we are unable to 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.
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.
WHY THIS FELLOWSHIP?
How is this program different from other data science programs?
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.
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 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.
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.
DAY-TO-DAY
What does the day-to-day look like?
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.
Fellows also have an opportunity to interact directly with our customers and get immediate feedback on their results.
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.
