When people join untapt, they engage in a conversation to help us understand them better. We ask, for example, what’s most important to them professionally (e.g., compensation, cool tech, work/life balance), where geographically they’d consider opportunities, and — the topic of this post! — what their job-seeking status is. As illustrated by the plot shown here, 96.3% of untapt
In this session, we discussed how to choose hyperparameters for neural networks, avoid learning slowdown, avoid overfitting, and initialize model weights.
Yesterday evening, untapt hosted the second of a series of workshops on Deep Learning. This one was focused on the backpropagation algorithm.
The risks associated with integrating algorithms into the talent acquisition process are appreciably offset by the benefits.
Last week, untapt hosted the inaugural session of a study group bent on mastering Deep Learning. It proved far more popular than we anticipated.
Deep Learning Study Group on Wednesday, August 17th at 6:30pm.
We crunched our rich internal database of hiring manager decisions to determine that the optimal resume length is 250 to 350 words.
Using our internal data, we were able to determine the optimal length of time required to make the ideal hire.
I recently had the honor of speaking to the New York chapter of Women in Machine Learning and Data Science about the field of data science and about how we leverage statistical modeling at untapt to predict the success of job applications. In this post I detail the former, leaving coverage of our in-house algorithm development
How academic scientists can transition into industry as Data Scientists.