Become a Data Science Mentor

Would you like to inspire the minds of working professionals with data science?

Our mentoring team is comprised of some of the best data scientists in the industry. By joining our mentoring team, you will become part of a strong network of battle-hardened data science professionals who have spent time in the trenches and on the front lines. We are partnering with leading data science companies to conduct training programs, host events, hire candidates, and more.

Why become a mentor?

Helping others walk the path you have taken to success is an enriching experience, and is a chance to show your leadership qualities among peers in the data science community.

Our community at Data Science Dojo, alone, is more than 5000 aspiring data scientists, practitioners, and all sorts of working professionals who use data on a daily basis. Many of our alumni have gone on to tackle meaningful, real-world data science projects in their companies or of their own pursuits after taking the bootcamp course. A large part of their drive and motivation to keep reaching towards further success has come from mentors; examples of others who were once like them in their journey of learning how to build on their skills.

Mentors are highly respected people at Data Science Dojo and become a voice for many in the community. Role models and leaders are of high value to keeping our community healthy and alive. Mentors at Data Science Dojo should be known and highlighted for the important role they play, and to share their story or journey to success. Mentors are featured on our website, and have the opportunity to be feature on our blog site, which reaches 10,000 actual users on average per month. Mentors also have the opportunity to publish their own blog, tutorial, or anything they take an interest in sharing with the community.

Of course, the direct interaction and impact on our bootcamp students and alumni is what makes the mentoring experience meaningful for most of our mentors. Our mentors enjoy close interaction with our students and can see how their positive impact results in more confident, more prepared, and more capable data nerds.

Easy-going, flexible mentoring

We are easy-going when it comes to mentors coaching students or alumni through their data science focused problems. We are happy to work with mentors’ busy schedules, and alert them to an opportunity to mentor when it arises. Mentors can choose to accept an opportunity, the number of expected hours to help students and alumni, and flexibility in negotiating the number of expected hours. We could expect half hour a week  for a few weeks to consult and coach a learner through a problem, for example, depending on the complexity of the problem. We are flexible to work with mentors’ schedules, as long as there is some consistency or commitment from mentors.

Some mentoring is paid work at Data Science Dojo, where the work requires more commitment to students and alumni than usual. For example, some mentors are interested in teaching opportunities and have instructed classes for our Bootcamp course.

Mentors are supported at Data Science Dojo, with the team on hand to answer any questions and help mentors and students/ alumni feel comfortable and acquainted with each other. Mentors may coach a small group together or an individual. All this is negotiable.

Margaux Penwarden

Data Scientist at McKinsey & Company

Margaux is a data scientist at McKinsey & Company, Sydney. Margaux holds a Bachelor’s in Computer Science and Mathematics from Télécom Paristech (“Grande Ecole”), and a Master’s in Statistics from Imperial College, London.

Melissa Dalis

Data Scientist at Uber

Melissa is a Data Scientist at Uber. Before Uber, Melissa worked as data scientist at Square supporting the product team with her own machine learning models. Melissa got a B.S in Computer Science and Mathematics with a minor in Economics from Duke University. Her thesis research was in computational game theory, applying game theoretic models to prevent cheating in casinos.

Roman Holenstein

Machine Learning Engineer at Apple

Roman obtained his Ph.D. in Computer Science from University of British Columbia. He worked on Monte Carlo framework method based on Markov chain and sequential Monte Carlo for efficient sampling from high-dimensional distributions. Roman specializes in Monte Carlo methods (MCMC, SMC), Bayesian statistics, machine learning, natural language understanding. He previously worked as a Senior Sceintist with Bing query understanding team at Microsoft.

Fill out the application below with your resume and cover letter