Frequently Asked Questions
PROGRAM BASICS & APPLICATION PROCESS
How long is the program?
The program is 3 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.
Who is eligible to apply?
Since the fellowship is remote, anyone can apply to the program. However, you must be a resident of a country not currently embargoed by the United States. We also cannot accept candidates from Australia due to labor laws in Australia.
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?
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.
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 happens to fellows after they graduate? What jobs do they get?
Who are the some of the commercial partners for Launchpad?
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 are expected to put in 30 hours of work per week. There are no predetermined fixed working hours. Hence fellows may allocate their time on their own.
Each fellow will work on the project of their choice with a team of ten fellows and a senior data scientist as a mentor.
Fellows are expected to join a daily 30 mins update call with their teammates.
Reading groups are an essential part of our program, where fellows take turns presenting recent AI publications every Thursday.
Each fellow will present their progress through a demo session hosted by the CEO on Fridays.
Fellows, during the program, will have full access to all the materials in our [Machine Learning and Data Science bootcamp](https://upskill.launchpad.ai/)
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 are expected to put in 30 hours of work per week. There are no predetermined fixed working hours. Hence fellows may allocate their time on their own.
Each fellow will work on the project of their choice with a team of ten fellows and a senior data scientist as a mentor.
Fellows are expected to join a daily 30 mins update call with their teammates.
Reading groups are an essential part of our program, where fellows take turns presenting recent AI publications every Thursday.
Each fellow will present their progress through a demo session hosted by the CEO on Fridays.
Fellows, during the program, will have full access to all the materials in our [Machine Learning and Data Science bootcamp](https://upskill.launchpad.ai/)
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.
