Jared Lander

Jared Lander

Chief Data Scientist
Lander Analytics
location_on United States

Member since 4 years


Jared Lander

Specialises In (based on submitted proposals)
machine-learning-&-deep-learning r elastic-net xgboost boosted-decision-trees glmnet coefplot machine-learning ai open-data-science boosted-trees gpu penalized-regression deep-neural-networks hyperparameters random-forest

Jared Lander is chief data scientist for Lander Analytics, where he oversees the long-term direction of the company and researches the best strategy, models, and algorithms for modern data needs. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization, and statistical computing. In addition to his client-facing consulting and training, Jared is an adjunct professor of statistics at Columbia University and the organizer of the New York Open Statistical Programming Meetup and the New York R Conference.

He is the author of R for Everyone, a book about R programming geared toward data scientists and non-statisticians alike. Very active in the data community, Jared is a frequent speaker at conferences, universities, and meetups around the world and was a member of the 2014 Strata New York selection committee. His writings on statistics can be found at Jaredlander.com. He was recently featured in the Wall Street Journal for his work with the Minnesota Vikings during the 2015 NFL Draft.

Jared holds a master’s degree in statistics from Columbia University and a bachelor’s degree in mathematics from Muhlenberg College.