You've made up your mind to become a data scientist. You've taken every data science MooC, you've eaten a lifetime of pizza at machine learning meetups, you even attended a data science "academy." Why hasn't it worked?
Data Science is not knowledge to be acquired but rather a skill that can be learned and improved through practice. The number one qualification employers look for when hiring a data science candidate is previous experience. Startup.ML is launching a fellowship to give aspiring data scientists the chance to hone their skills by building real machine learning applications for startups and established data science teams.
Upon completing the program, fellows will have direct access to a network of hiring partners, a letter of recommendation, and a portfolio of real world projects.
Full time for 4 months at our San Francisco location
Fellows build scalable machine learning models that integrate into real products
Experienced data scientist working in industry offer mentorship and support
We follow an agile development process in groups of 3 (pair programming plus agile team lead). This means weekly iteration planning, daily scrum and every Friday is demo day!
We strive to have each fellow work on two kinds of projects to give them exposure to the full range of problems they will confront in industry.
We ensure that our fellows work on real world problems sourced through partnerships with established data science teams. This aspect of the work is well defined, large datasets have already been gathered, and there is an opportunity to learn from leading practitioners.
The challenge fellows will typically confront is how to get results when working in a large team with lots of differing opinions, approaches, skills and priorities.
Another key skill that fellows learn by working with an established data science team, is the ability to communicate ideas and how to build consensus. We ask fellows to present their work to outside team members on a weekly basis which hones their ability to deliver crisp results.
Startup.ML works with early stage companies in the IoT and Health 2.0 space to help them incorporate machine learning into their products. We afford fellows the opportunity to work on these problems along with us. The learning opportunity for this part of the fellowship is different from the previous experience of working with an established data science team.
There may be a lack of clarity about what exactly needs to be done, the data may not exist (or if it exists it often doesn't have the necessary signal), the founders are uncertain about the direction they want to take their product, etc.
Fellows need to learn how to iterate quickly and laser-focus on a minimum viable product.
Data Science is an interdisciplinary field which combines aspects of computer science, mathematics and statistics. Rarely do we see someone that has a deep background in all of these areas. We encounter software engineers that are not familiar with probabilistic approaches to problem solving and quants (mathematicians, statisticians, physicists, computational biologists, etc.) that can't write a recursive function.
Instead of holding out for the perfect unicorn, we pair fellows from each of these backgrounds to work on a problem together. The pair-programming methodology has already proven to be extremely effective in agile software development and we believe it’s also the right approach for data science.
While it is true that the problem should dictate the choice of the tool, being active practitioners in the field we have developed some preferences.
Julia is a new but very promising language for scientific compute. We have successfully used Julia for digital signal processing, state-space tracking and anomaly detection problems.
Fellows are free to pick their choice of tools but using a tool that mentors are familiar with, makes it easier to get help.
At the completion of the program, we help fellows find the right career opportunity in a data science field. Fellows gain intimate knowledge of the startups and the work being done by established data science teams, so there are many options to pursue:
becoming the first data scientist at the startup
joining the established data science team
exploring opportunities with Startup.ML’s industry partners
We are accepting applications for the July cohort.
Data science is an incredibly powerful art, practiced by an incredibly small community of artists. We are trying to change this! Our objective is to grow the data science community by 500+ active practitioners over the next 3 years. We want fellows to become the in-house data scientist of one of our startups or join our partner network of data science teams.
Someone with 2-5 years of professional experience with either a software engineering or quantitative analysis background that has decided to pursue data science as a career. PhD/MS is not required however most quants that apply successfully have some level of graduate training.
Currently the program is only offered in San Francisco.
Due to the immersive nature of learning experience, part-time / telecommuting is not an option.
Generally the answer is "No." The program is designed to provide last-mile experience for those ready to start a data science career. In rare cases we'll consider the application of someone that is still in school if their research work could lead to a promising startup.
Our fellowship program is completely free.
Absolutely! If you have an interesting startup idea in IoT, Health 2.0 or other areas and want to build your first prototype during the fellowship, we'd love to help.
There are many ways to get involved in growing the data science community. You can help us mentor fellows, become a hiring partner or even provide a small project for the fellows to work on.